what is research hypothesis in education

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

• What is Educational Research? + [Types, Scope & Importance]

Education is an integral aspect of every society and in a bid to expand the frontiers of knowledge, educational research must become a priority. Educational research plays a vital role in the overall development of pedagogy, learning programs, and policy formulation. 

Educational research is a spectrum that bothers on multiple fields of knowledge and this means that it draws from different disciplines. As a result of this, the findings of this research are multi-dimensional and can be restricted by the characteristics of the research participants and the research environment. 

What is Educational Research?

Educational research is a type of systematic investigation that applies empirical methods to solving challenges in education. It adopts rigorous and well-defined scientific processes in order to gather and analyze data for problem-solving and knowledge advancement. 

J. W. Best defines educational research as that activity that is directed towards the development of a science of behavior in educational situations. The ultimate aim of such a science is to provide knowledge that will permit the educator to achieve his goals through the most effective methods.

The primary purpose of educational research is to expand the existing body of knowledge by providing solutions to different problems in pedagogy while improving teaching and learning practices. Educational researchers also seek answers to questions bothering on learner motivation, development, and classroom management. 

Characteristics of Education Research  

While educational research can take numerous forms and approaches, several characteristics define its process and approach. Some of them are listed below:

• It sets out to solve a specific problem.
• Educational research adopts primary and secondary research methods in its data collection process . This means that in educational research, the investigator relies on first-hand sources of information and secondary data to arrive at a suitable conclusion. 
• Educational research relies on empirical evidence . This results from its largely scientific approach.
• Educational research is objective and accurate because it measures verifiable information.
• In educational research, the researcher adopts specific methodologies, detailed procedures, and analysis to arrive at the most objective responses
• Educational research findings are useful in the development of principles and theories that provide better insights into pressing issues.
• This research approach combines structured, semi-structured, and unstructured questions to gather verifiable data from respondents.
• Many educational research findings are documented for peer review before their presentation. 
• Educational research is interdisciplinary in nature because it draws from different fields and studies complex factual relations.

Types of Educational Research 

Educational research can be broadly categorized into 3 which are descriptive research , correlational research , and experimental research . Each of these has distinct and overlapping features. 

Descriptive Educational Research

In this type of educational research, the researcher merely seeks to collect data with regards to the status quo or present situation of things. The core of descriptive research lies in defining the state and characteristics of the research subject being understudied. 

Because of its emphasis on the “what” of the situation, descriptive research can be termed an observational research method . In descriptive educational research, the researcher makes use of quantitative research methods including surveys and questionnaires to gather the required data.

Typically, descriptive educational research is the first step in solving a specific problem. Here are a few examples of descriptive research: 

• A reading program to help you understand student literacy levels.
• A study of students’ classroom performance.
• Research to gather data on students’ interests and preferences. 

From these examples, you would notice that the researcher does not need to create a simulation of the natural environment of the research subjects; rather, he or she observes them as they engage in their routines. Also, the researcher is not concerned with creating a causal relationship between the research variables. 

Correlational Educational Research

This is a type of educational research that seeks insights into the statistical relationship between two research variables. In correlational research, the researcher studies two variables intending to establish a connection between them. 

Correlational research can be positive, negative, or non-existent. Positive correlation occurs when an increase in variable A leads to an increase in variable B, while negative correlation occurs when an increase in variable A results in a decrease in variable B. 

When a change in any of the variables does not trigger a succeeding change in the other, then the correlation is non-existent. Also, in correlational educational research, the research does not need to alter the natural environment of the variables; that is, there is no need for external conditioning. 

Examples of educational correlational research include: 

• Research to discover the relationship between students’ behaviors and classroom performance.
• A study into the relationship between students’ social skills and their learning behaviors. 

Experimental Educational Research

Experimental educational research is a research approach that seeks to establish the causal relationship between two variables in the research environment. It adopts quantitative research methods in order to determine the cause and effect in terms of the research variables being studied. 

Experimental educational research typically involves two groups – the control group and the experimental group. The researcher introduces some changes to the experimental group such as a change in environment or a catalyst, while the control group is left in its natural state. 

The introduction of these catalysts allows the researcher to determine the causative factor(s) in the experiment. At the core of experimental educational research lies the formulation of a hypothesis and so, the overall research design relies on statistical analysis to approve or disprove this hypothesis.

Examples of Experimental Educational Research

• A study to determine the best teaching and learning methods in a school.
• A study to understand how extracurricular activities affect the learning process. 

Based on functionality, educational research can be classified into fundamental research , applied research , and action research. The primary purpose of fundamental research is to provide insights into the research variables; that is, to gain more knowledge. Fundamental research does not solve any specific problems. 

Just as the name suggests, applied research is a research approach that seeks to solve specific problems. Findings from applied research are useful in solving practical challenges in the educational sector such as improving teaching methods, modifying learning curricula, and simplifying pedagogy. 

Action research is tailored to solve immediate problems that are specific to a context such as educational challenges in a local primary school. The goal of action research is to proffer solutions that work in this context and to solve general or universal challenges in the educational sector. 

Importance of Educational Research

• Educational research plays a crucial role in knowledge advancement across different fields of study. 
• It provides answers to practical educational challenges using scientific methods.
• Findings from educational research; especially applied research, are instrumental in policy reformulation. 
• For the researcher and other parties involved in this research approach, educational research improves learning, knowledge, skills, and understanding.
• Educational research improves teaching and learning methods by empowering you with data to help you teach and lead more strategically and effectively.
• Educational research helps students apply their knowledge to practical situations.

Educational Research Methods 

• Surveys/Questionnaires

A survey is a research method that is used to collect data from a predetermined audience about a specific research context. It usually consists of a set of standardized questions that help you to gain insights into the experiences, thoughts, and behaviors of the audience. 

Surveys can be administered physically using paper forms, face-to-face conversations, telephone conversations, or online forms. Online forms are easier to administer because they help you to collect accurate data and to also reach a larger sample size. Creating your online survey on data-gathering platforms like Formplus allows you to.also analyze survey respondent’s data easily. 

In order to gather accurate data via your survey, you must first identify the research context and the research subjects that would make up your data sample size. Next, you need to choose an online survey tool like Formplus to help you create and administer your survey with little or no hassles. 

An interview is a qualitative data collection method that helps you to gather information from respondents by asking questions in a conversation. It is typically a face-to-face conversation with the research subjects in order to gather insights that will prove useful to the specific research context. 

Interviews can be structured, semi-structured , or unstructured . A structured interview is a type of interview that follows a premeditated sequence; that is, it makes use of a set of standardized questions to gather information from the research subjects. 

An unstructured interview is a type of interview that is fluid; that is, it is non-directive. During a structured interview, the researcher does not make use of a set of predetermined questions rather, he or she spontaneously asks questions to gather relevant data from the respondents. 

A semi-structured interview is the mid-point between structured and unstructured interviews. Here, the researcher makes use of a set of standardized questions yet, he or she still makes inquiries outside these premeditated questions as dedicated by the flow of the conversations in the research context. 

Data from Interviews can be collected using audio recorders, digital cameras, surveys, and questionnaires. 

• Observation

Observation is a method of data collection that entails systematically selecting, watching, listening, reading, touching, and recording behaviors and characteristics of living beings, objects, or phenomena. In the classroom, teachers can adopt this method to understand students’ behaviors in different contexts. 

Observation can be qualitative or quantitative in approach . In quantitative observation, the researcher aims at collecting statistical information from respondents and in qualitative information, the researcher aims at collecting qualitative data from respondents. 

Qualitative observation can further be classified into participant or non-participant observation. In participant observation, the researcher becomes a part of the research environment and interacts with the research subjects to gather info about their behaviors. In non-participant observation, the researcher does not actively take part in the research environment; that is, he or she is a passive observer. 

How to Create Surveys and Questionnaires with Formplus

• On your dashboard, choose the “create new form” button to access the form builder. You can also choose from the available survey templates and modify them to suit your need.
• Save your online survey to access the form customization section. Here, you can change the physical appearance of your form by adding preferred background images and inserting your organization’s logo.
• Formplus has a form analytics dashboard that allows you to view insights from your data collection process such as the total number of form views and form submissions. You can also use the reports summary tool to generate custom graphs and charts from your survey data. 

Steps in Educational Research

Like other types of research, educational research involves several steps. Following these steps allows the researcher to gather objective information and arrive at valid findings that are useful to the research context. 

• Define the research problem clearly. 
• Formulate your hypothesis. A hypothesis is the researcher’s reasonable guess based on the available evidence, which he or she seeks to prove in the course of the research.
• Determine the methodology to be adopted. Educational research methods include interviews, surveys, and questionnaires.
• Collect data from the research subjects using one or more educational research methods. You can collect research data using Formplus forms.
• Analyze and interpret your data to arrive at valid findings. In the Formplus analytics dashboard, you can view important data collection insights and you can also create custom visual reports with the reports summary tool. 
• Create your research report. A research report details the entire process of the systematic investigation plus the research findings. 

Conclusion 

Educational research is crucial to the overall advancement of different fields of study and learning, as a whole. Data in educational research can be gathered via surveys and questionnaires, observation methods, or interviews – structured, unstructured, and semi-structured. 

You can create a survey/questionnaire for educational research with Formplu s. As a top-tier data tool, Formplus makes it easy for you to create your educational research survey in the drag-and-drop form builder, and share this with survey respondents using one or more of the form sharing options. 

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• education research
• educational research types
• examples of educational research
• importance of educational research
• purpose of educational research
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Definition of a Hypothesis

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

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Neag School of Education

Educational Research Basics by Del Siegle

Null and alternative hypotheses.

Converting research questions to hypothesis is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists between the groups (experiment study) and you have the alternative hypothesis. Write the statement such that a relationship does not exist or a difference does not exist and you have the null hypothesis. You can reverse the process if you have a hypothesis and wish to write a research question.

When you are comparing two groups, the groups are the independent variable. When you are testing whether something affects something else, the cause is the independent variable. The independent variable is the one you manipulate.

Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay. The first step is to ask yourself “Are there two or more groups being compared?” The answer is “Yes.” What are the groups? Teachers who are given higher pay and teachers who are given lower pay. The independent variable is teacher pay. The dependent variable (the outcome) is attitude towards school.

You could also approach is another way. “Is something causing something else?” The answer is “Yes.”  What is causing what? Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

By tradition, we try to disprove (reject) the null hypothesis. We can never prove a null hypothesis, because it is impossible to prove something does not exist. We can disprove something does not exist by finding an example of it. Therefore, in research we try to disprove the null hypothesis. When we do find that a relationship (or difference) exists then we reject the null and accept the alternative. If we do not find that a relationship (or difference) exists, we fail to reject the null hypothesis (and go with it). We never say we accept the null hypothesis because it is never possible to prove something does not exist. That is why we say that we failed to reject the null hypothesis, rather than we accepted it.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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Research Topics & Ideas: Education

170+ Research Ideas To Fast-Track Your Dissertation, Thesis Or Research Project

I f you’re just starting out exploring education-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from actual dissertations and theses..

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Education Research Topics

• How to find a research topic (video)
• List of 50+ education-related research topics/ideas
• List of 120+ level-specific research topics 
• Examples of actual dissertation topics in education
• Tips to fast-track your topic ideation (video)
• Where to get extra help

Education-Related Research Topics & Ideas

Below you’ll find a list of education-related research topics and idea kickstarters. These are fairly broad and flexible to various contexts, so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• The impact of school funding on student achievement
• The effects of social and emotional learning on student well-being
• The effects of parental involvement on student behaviour
• The impact of teacher training on student learning
• The impact of classroom design on student learning
• The impact of poverty on education
• The use of student data to inform instruction
• The role of parental involvement in education
• The effects of mindfulness practices in the classroom
• The use of technology in the classroom
• The role of critical thinking in education
• The use of formative and summative assessments in the classroom
• The use of differentiated instruction in the classroom
• The use of gamification in education
• The effects of teacher burnout on student learning
• The impact of school leadership on student achievement
• The effects of teacher diversity on student outcomes
• The role of teacher collaboration in improving student outcomes
• The implementation of blended and online learning
• The effects of teacher accountability on student achievement
• The effects of standardized testing on student learning
• The effects of classroom management on student behaviour
• The effects of school culture on student achievement
• The use of student-centred learning in the classroom
• The impact of teacher-student relationships on student outcomes
• The achievement gap in minority and low-income students
• The use of culturally responsive teaching in the classroom
• The impact of teacher professional development on student learning
• The use of project-based learning in the classroom
• The effects of teacher expectations on student achievement
• The use of adaptive learning technology in the classroom
• The impact of teacher turnover on student learning
• The effects of teacher recruitment and retention on student learning
• The impact of early childhood education on later academic success
• The impact of parental involvement on student engagement
• The use of positive reinforcement in education
• The impact of school climate on student engagement
• The role of STEM education in preparing students for the workforce
• The effects of school choice on student achievement
• The use of technology in the form of online tutoring

Level-Specific Research Topics

Looking for research topics for a specific level of education? We’ve got you covered. Below you can find research topic ideas for primary, secondary and tertiary-level education contexts. Click the relevant level to view the respective list.

Research Topics: Pick An Education Level

Primary education.

• Investigating the effects of peer tutoring on academic achievement in primary school
• Exploring the benefits of mindfulness practices in primary school classrooms
• Examining the effects of different teaching strategies on primary school students’ problem-solving skills
• The use of storytelling as a teaching strategy in primary school literacy instruction
• The role of cultural diversity in promoting tolerance and understanding in primary schools
• The impact of character education programs on moral development in primary school students
• Investigating the use of technology in enhancing primary school mathematics education
• The impact of inclusive curriculum on promoting equity and diversity in primary schools
• The impact of outdoor education programs on environmental awareness in primary school students
• The influence of school climate on student motivation and engagement in primary schools
• Investigating the effects of early literacy interventions on reading comprehension in primary school students
• The impact of parental involvement in school decision-making processes on student achievement in primary schools
• Exploring the benefits of inclusive education for students with special needs in primary schools
• Investigating the effects of teacher-student feedback on academic motivation in primary schools
• The role of technology in developing digital literacy skills in primary school students
• Effective strategies for fostering a growth mindset in primary school students
• Investigating the role of parental support in reducing academic stress in primary school children
• The role of arts education in fostering creativity and self-expression in primary school students
• Examining the effects of early childhood education programs on primary school readiness
• Examining the effects of homework on primary school students’ academic performance
• The role of formative assessment in improving learning outcomes in primary school classrooms
• The impact of teacher-student relationships on academic outcomes in primary school
• Investigating the effects of classroom environment on student behavior and learning outcomes in primary schools
• Investigating the role of creativity and imagination in primary school curriculum
• The impact of nutrition and healthy eating programs on academic performance in primary schools
• The impact of social-emotional learning programs on primary school students’ well-being and academic performance
• The role of parental involvement in academic achievement of primary school children
• Examining the effects of classroom management strategies on student behavior in primary school
• The role of school leadership in creating a positive school climate Exploring the benefits of bilingual education in primary schools
• The effectiveness of project-based learning in developing critical thinking skills in primary school students
• The role of inquiry-based learning in fostering curiosity and critical thinking in primary school students
• The effects of class size on student engagement and achievement in primary schools
• Investigating the effects of recess and physical activity breaks on attention and learning in primary school
• Exploring the benefits of outdoor play in developing gross motor skills in primary school children
• The effects of educational field trips on knowledge retention in primary school students
• Examining the effects of inclusive classroom practices on students’ attitudes towards diversity in primary schools
• The impact of parental involvement in homework on primary school students’ academic achievement
• Investigating the effectiveness of different assessment methods in primary school classrooms
• The influence of physical activity and exercise on cognitive development in primary school children
• Exploring the benefits of cooperative learning in promoting social skills in primary school students

Secondary Education

• Investigating the effects of school discipline policies on student behavior and academic success in secondary education
• The role of social media in enhancing communication and collaboration among secondary school students
• The impact of school leadership on teacher effectiveness and student outcomes in secondary schools
• Investigating the effects of technology integration on teaching and learning in secondary education
• Exploring the benefits of interdisciplinary instruction in promoting critical thinking skills in secondary schools
• The impact of arts education on creativity and self-expression in secondary school students
• The effectiveness of flipped classrooms in promoting student learning in secondary education
• The role of career guidance programs in preparing secondary school students for future employment
• Investigating the effects of student-centered learning approaches on student autonomy and academic success in secondary schools
• The impact of socio-economic factors on educational attainment in secondary education
• Investigating the impact of project-based learning on student engagement and academic achievement in secondary schools
• Investigating the effects of multicultural education on cultural understanding and tolerance in secondary schools
• The influence of standardized testing on teaching practices and student learning in secondary education
• Investigating the effects of classroom management strategies on student behavior and academic engagement in secondary education
• The influence of teacher professional development on instructional practices and student outcomes in secondary schools
• The role of extracurricular activities in promoting holistic development and well-roundedness in secondary school students
• Investigating the effects of blended learning models on student engagement and achievement in secondary education
• The role of physical education in promoting physical health and well-being among secondary school students
• Investigating the effects of gender on academic achievement and career aspirations in secondary education
• Exploring the benefits of multicultural literature in promoting cultural awareness and empathy among secondary school students
• The impact of school counseling services on student mental health and well-being in secondary schools
• Exploring the benefits of vocational education and training in preparing secondary school students for the workforce
• The role of digital literacy in preparing secondary school students for the digital age
• The influence of parental involvement on academic success and well-being of secondary school students
• The impact of social-emotional learning programs on secondary school students’ well-being and academic success
• The role of character education in fostering ethical and responsible behavior in secondary school students
• Examining the effects of digital citizenship education on responsible and ethical technology use among secondary school students
• The impact of parental involvement in school decision-making processes on student outcomes in secondary schools
• The role of educational technology in promoting personalized learning experiences in secondary schools
• The impact of inclusive education on the social and academic outcomes of students with disabilities in secondary schools
• The influence of parental support on academic motivation and achievement in secondary education
• The role of school climate in promoting positive behavior and well-being among secondary school students
• Examining the effects of peer mentoring programs on academic achievement and social-emotional development in secondary schools
• Examining the effects of teacher-student relationships on student motivation and achievement in secondary schools
• Exploring the benefits of service-learning programs in promoting civic engagement among secondary school students
• The impact of educational policies on educational equity and access in secondary education
• Examining the effects of homework on academic achievement and student well-being in secondary education
• Investigating the effects of different assessment methods on student performance in secondary schools
• Examining the effects of single-sex education on academic performance and gender stereotypes in secondary schools
• The role of mentoring programs in supporting the transition from secondary to post-secondary education

Tertiary Education

• The role of student support services in promoting academic success and well-being in higher education
• The impact of internationalization initiatives on students’ intercultural competence and global perspectives in tertiary education
• Investigating the effects of active learning classrooms and learning spaces on student engagement and learning outcomes in tertiary education
• Exploring the benefits of service-learning experiences in fostering civic engagement and social responsibility in higher education
• The influence of learning communities and collaborative learning environments on student academic and social integration in higher education
• Exploring the benefits of undergraduate research experiences in fostering critical thinking and scientific inquiry skills
• Investigating the effects of academic advising and mentoring on student retention and degree completion in higher education
• The role of student engagement and involvement in co-curricular activities on holistic student development in higher education
• The impact of multicultural education on fostering cultural competence and diversity appreciation in higher education
• The role of internships and work-integrated learning experiences in enhancing students’ employability and career outcomes
• Examining the effects of assessment and feedback practices on student learning and academic achievement in tertiary education
• The influence of faculty professional development on instructional practices and student outcomes in tertiary education
• The influence of faculty-student relationships on student success and well-being in tertiary education
• The impact of college transition programs on students’ academic and social adjustment to higher education
• The impact of online learning platforms on student learning outcomes in higher education
• The impact of financial aid and scholarships on access and persistence in higher education
• The influence of student leadership and involvement in extracurricular activities on personal development and campus engagement
• Exploring the benefits of competency-based education in developing job-specific skills in tertiary students
• Examining the effects of flipped classroom models on student learning and retention in higher education
• Exploring the benefits of online collaboration and virtual team projects in developing teamwork skills in tertiary students
• Investigating the effects of diversity and inclusion initiatives on campus climate and student experiences in tertiary education
• The influence of study abroad programs on intercultural competence and global perspectives of college students
• Investigating the effects of peer mentoring and tutoring programs on student retention and academic performance in tertiary education
• Investigating the effectiveness of active learning strategies in promoting student engagement and achievement in tertiary education
• Investigating the effects of blended learning models and hybrid courses on student learning and satisfaction in higher education
• The role of digital literacy and information literacy skills in supporting student success in the digital age
• Investigating the effects of experiential learning opportunities on career readiness and employability of college students
• The impact of e-portfolios on student reflection, self-assessment, and showcasing of learning in higher education
• The role of technology in enhancing collaborative learning experiences in tertiary classrooms
• The impact of research opportunities on undergraduate student engagement and pursuit of advanced degrees
• Examining the effects of competency-based assessment on measuring student learning and achievement in tertiary education
• Examining the effects of interdisciplinary programs and courses on critical thinking and problem-solving skills in college students
• The role of inclusive education and accessibility in promoting equitable learning experiences for diverse student populations
• The role of career counseling and guidance in supporting students’ career decision-making in tertiary education
• The influence of faculty diversity and representation on student success and inclusive learning environments in higher education

Education-Related Dissertations & Theses

While the ideas we’ve presented above are a decent starting point for finding a research topic in education, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses in the education space to see how this all comes together in practice.

Below, we’ve included a selection of education-related research projects to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• From Rural to Urban: Education Conditions of Migrant Children in China (Wang, 2019)
• Energy Renovation While Learning English: A Guidebook for Elementary ESL Teachers (Yang, 2019)
• A Reanalyses of Intercorrelational Matrices of Visual and Verbal Learners’ Abilities, Cognitive Styles, and Learning Preferences (Fox, 2020)
• A study of the elementary math program utilized by a mid-Missouri school district (Barabas, 2020)
• Instructor formative assessment practices in virtual learning environments : a posthumanist sociomaterial perspective (Burcks, 2019)
• Higher education students services: a qualitative study of two mid-size universities’ direct exchange programs (Kinde, 2020)
• Exploring editorial leadership : a qualitative study of scholastic journalism advisers teaching leadership in Missouri secondary schools (Lewis, 2020)
• Selling the virtual university: a multimodal discourse analysis of marketing for online learning (Ludwig, 2020)
• Advocacy and accountability in school counselling: assessing the use of data as related to professional self-efficacy (Matthews, 2020)
• The use of an application screening assessment as a predictor of teaching retention at a midwestern, K-12, public school district (Scarbrough, 2020)
• Core values driving sustained elite performance cultures (Beiner, 2020)
• Educative features of upper elementary Eureka math curriculum (Dwiggins, 2020)
• How female principals nurture adult learning opportunities in successful high schools with challenging student demographics (Woodward, 2020)
• The disproportionality of Black Males in Special Education: A Case Study Analysis of Educator Perceptions in a Southeastern Urban High School (McCrae, 2021)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic within education, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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How To Choose A Research Topic: 5 Key Criteria

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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See an example

The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?

Affiliations.

• 1 Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC.
• 2 Informatics Institute, School of Medicine, University of Alabama, Birmingham, Birmingham, AL.
• 3 Cognitive Studies in Medicine and Public Health, The New York Academy of Medicine, New York City, NY.
• 4 Department of Educational Studies, Patton College of Education, Ohio University, Athens, OH.
• 5 Department of Clinical Sciences and Community Health, Touro University California College of Osteopathic Medicine, Vallejo, CA.
• 6 Department of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, OH.
• 7 Department of Health Science, California State University Channel Islands, Camarillo, CA.
• PMID: 39211055
• PMCID: PMC11361316
• DOI: 10.18103/mra.v12i2.5132

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS-a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

Keywords: Clinical research; data-driven hypothesis generation; medical informatics; scientific hypothesis generation; translational research; visualization.

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American Fellowships

Funding:  $8,000–$50,000 Opens: September 3 Deadline:  November 15

The American Fellowship program began in 1888, a time when women were discouraged from pursuing an education. It is AAUW’s largest fellowship program and the oldest non-institutional source of graduate funding for women in the United States.  

AAUW American Fellowships support women scholars who are pursuing full-time study to complete dissertations, conducting postdoctoral research full time, or preparing research for publication for eight consecutive weeks. Applicants must be U.S. citizens or permanent residents. Candidates are evaluated based on scholarly excellence; quality and originality of project design; and active commitment to helping women and girls through service in their communities, professions, or fields of research.  

Dissertation: The purpose of the American Dissertation Fellowship is to offset a scholar’s living expenses while they complete their dissertation. F ellows must use the award for the final year of writing the dissertation. Applicants must have completed all course work, passed all preliminary examinations, and received approval for their research proposals or plans by the preceding November. Students holding fellowships for writing a dissertation in the year prior to the AAUW fellowships year are not eligible. Open to applicants in all fields of study. Scholars engaged in science, technology, engineering , and math fields or those researching gender issues are especially encouraged to apply.  

Postdoctoral: The primary purpose of the American Postdoctoral Research Leave Fellowship is to increase the number of women in tenure-track faculty positions and to promote equity for women in higher education. This fellowship ’s purpose is to assist the candidate in obtaining tenure and further promotions by enabling them to spend a year pursuing independent research. Tenured professors are not eligible. Open to applicants in all fields of study. Scholars engaged in science, technology, engineering , and math fields or those researching gender issues are especially encouraged to apply.  

Publication: The Short-Term Research Publication Grants provide support to scholars to prepare research manuscripts for publication. AAUW’s funding priority is for applicants whose work supports the vision of AAUW: to break through educational and economic barriers so that all women have a fair chance. Time must be available for eight consecutive weeks of final writing and editing in response to issues raised in critical reviews. These fellowships can be for both tenure-track and part-time faculty, and to new and established researchers. The purpose is to assist the candidate in obtaining tenure and other promotions. Tenured professors are not eligible. Open to applicants in all fields of study. Scholars engaged in science, technology, engineering , and math fields or those researching gender issues are especially encouraged to apply.  

Award Amount

Dissertation Fellowship: $25,000

Postdoctoral Research Leave Fellowship: $50,000

Short-Term Research Publication Grant: $8,000

September 3, 2024 Application opens.

November 15, 2024, by 11:59 p.m. Pacific Standard Time Deadline for online submission of application, recommendations, and supporting documents.

April 15, 2025 Notification of decision emailed to all applicants. AAUW is not able to honor requests for earlier notification.

July 1, 2025–June 30, 2026 Fellowship year

When a date falls on a weekend or holiday, the date will be observed on the following business day.  

Eligibility

Applicants of all American Fellowships must meet the following criteria:  

• Members of the AAUW Board of Directors, committees, panels, task forces and staff, including current interns, are not eligible to apply for AAUW’s fellowships and grants. A person holding a current award is eligible for election or appointment to boards, committees, panels and task forces.  
• American Fellowship candidates must be U.S. citizens or permanent residents.  
• Fellowships are open to women, including people who identify as women, in all fields of study at an accredited institution of higher education. AAUW will make final decisions about what constitutes eligible institutions.  
• Applicants may not apply for another AAUW national fellowship or grant in the same year.  
• Distance learning/online programs: Fellowships support traditional classroom-based courses of study at colleges or universities. This fellowship program does not provide funding for distance learning or online programs or for degrees heavily dependent on distance learning components. Final decisions about what constitutes distance learning under these fellowships will be made by AAUW. AAUW will accept applications from applicants who are temporarily studying remotely due to COVID-19 precautions at their institution.  
• American Fellowships are not open to previous recipients of any AAUW national fellowship or grant (not including branch or local awards or Community Action Grants).

A pplicants of Dissertation Fellowships must also meet the following criteria :  

• The American Dissertation Fellowship must be used for the final year of writing the dissertation. Applicants must have completed all coursework, passed all preliminary exams, and had the dissertation research proposal or plan approved by November 1, 2023 . The doctoral degree/dissertation must be completed between April 1 and June 30, 2025 . Degree conferral must be between April 1 and September 15, 2025 .  
• Dissertation Fellows are not required to study in the U.S.  
• Students already holding a fellowship or grant for the purpose of supporting their final year of writing or completing the dissertation the year before the fellowship year are not eligible to apply for the American Dissertation Fellowship.  
• The Dissertation Fellowship is intended for applicants who are completing their first doctoral degree.  
• Applicants may apply up to two times for a fellowship for the same dissertation project.  

A pplicants of Postdoctoral Fellowships must also meet the following criteria :  

• American Postdoctoral Research Leave Fellowship applicants must hold a Ph.D., Ed.D., D.B.A., M.F.A., J.D., M.D., D.M.D., D.V.M., D.S.W., or M.P.H. at the time of application.  
• Tenured professors are not eligible.  

Applicants of Publication Grants must also meet the following criteria :  

• American Short-Term Research Publication Grant applicants must hold a Ph.D., Ed.D., D.B.A., M.F.A., J.D., M.D., D.M.D., D.V.M., D.S.W., or M.P.H. at the time of application.  
• Tenured professors are not eligible.
• American Short-Term Research Publication Grants are for tenure-track, part-time, and temporary faculty, as well as new and established researchers at universities. Scholars with strong publication records should seek funding elsewhere. Applicants must have time available for eight consecutive weeks of final manuscript preparation. While many recipients, especially full-time faculty members, will use the award s during the summer, recipients may use the funds at any time during the award year. Applicants must demonstrate that the support will result in a reduction of their ongoing work-related activities during the eight-week period .  
• American Short-Term Research Publication Grants are not for preliminary research. Activities undertaken during the grant period can include drafting, editing, or modifying manuscripts; replicating research components; responding to issues raised through critical review; and other initiatives to increase the likelihood of publication.  
• The grantee must be listed as the sole author, senior author, first author, or an author of equivalent significance.  

Selection Criteria and Application Review

The panel meets once a year to review applications for funding. Awards are based on the criteria outlined here. The panel’s recommendations are subject to final approval by AAUW. Fellowships are awarded on a competitive basis according to funds available in a given fiscal year.  

To ensure a fair review process, AAUW does not comment on the deliberations of the award panels. AAUW does not provide evaluations of applications. No provisions exist for reconsidering fellowship proposals.

Applications and supporting documents become the sole property of AAUW and will not be returned or held for another year.  

In selecting fellowship recipients, the following criteria will be considered:  

• Applicant’s scholarly excellence.  
• Quality of project design.  
• Originality of project.  
• Scholarly significance of project to the discipline.  
• Feasibility of project and proposed schedule.  
• Qualifications of applicant.  
• Applicant’s commitment to women’s issues in the profession/community.  
• Applicant’s mentoring of other women.  
• Applicant’s teaching experience.  
• Potential of applicant to make a significant contribution to the field.  
• Applicant is from an underrepresented racial/ethnic background.  
• Applicant will be in an underrepresented area of the country and/or type of university other than a top-level research institution during the award year.  
• Financial need.  

The primary criterion for fellowship awards is scholarly excellence. Applications are reviewed by distinguished scholars and should be prepared accordingly.  

American Postdoctoral Research Leave Fellowship and American Short-Term Research Publication Grant: When comparing proposals of equal merit, the review panel will give special consideration to women holding junior academic appointments who are seeking research leave, women who have held the doctorate for at least three years, and women whose educational careers have been interrupted. Preference will also be given to projects that are not simply a revision of the applicant’s doctoral dissertation and applicants whose work supports the vision of AAUW: to break through educational and economic barriers so that all women have a fair chance.  

Regulations

American Fellowships funds are available for:  

• Educational expenses (American Dissertation Fellowship and American Postdoctoral Research Leave Fellowship only).  
• Living expenses.  
• Dependent child care.  
• Travel to professional meetings, conferences, or seminars that does not exceed 10 percent of the fellowship total (American Dissertation Fellowship and American Postdoctoral Research Leave Fellowship only).  

Additionally, American Short-Term Publication Grant funds are available for:  

• Clerical and technical support.  
• Research assistance related to verification (not basic research).  
• Office supplies, postage, copying and related expenses.  
• Journal fees.  

American Fellowships funds are not available for:  

• Purchase of equipment.  
• Indirect costs.  
• Research assistants.  
• Previous expenditures, deficits, or repayment of loans.  
• Publication costs (except for American Short-Term Publication Grants).  
• Institutional (overhead) costs.  
• Tuition for dependent’s education.  
• Tuition for coursework that is in addition to credits required for maintaining full-time status while completing a dissertation.  
• Extended field research (applicable to American Dissertation Fellowships only).  

Additionally, American Short-Term Research Publication Grants funds are not available for:  

• Salary increase.  
• Doctoral dissertation research or writing.  

AAUW regards the acceptance of a fellowship as a contract requiring fulfillment of the following terms:  

• All American Fellowship recipients are required to sign a contract as acceptance of the award. Retain these instructions as they will become part of the fellowship contract if the applicant is awarded a fellowship.  
• An AAUW American Fellow is expected to pursue their project full time during the funding period (July 1–June 30). No partial fellowships are awarded. Fellowships may not be deferred.  
• American Postdoctoral Research Leave Fellows and American Short-Term Research Publication Grantees cannot pursue a degree during the award period.  
• Any major changes in plans for the award year must have prior written approval from AAUW.  
• AAUW must be notified promptly of any change in the status of an application resulting from acceptance of another award.  
• Stipends are made payable to fellows, not to institutions.  
• The determination of whether there is a tax obligation associated with the receipt of an AAUW award is the sole responsibility of the applicant. Specific questions regarding income tax matters should be addressed with the U.S. Internal Revenue Service, the applicant’s financial aid office or a personal tax adviser. AAUW cannot provide tax advice. AAUW is a nonprofit, tax-exempt 501(c)(3) public charity founded for educational purposes.  

Required Components*

Start the application process by clicking the Apply Now button below to access the application and create an account through our vendor site. Complete all required components in the following tabs.  

• Recommendations: Standardized or form-letter recommendations are discouraged. AAUW does not accept references from dossier services such as Parment or Interfolio.
• Dissertation Fellowship applicants: Applicant must provide two recommendations from the applicant’s advisers, colleagues or others well acquainted with the applicant, their project and their teaching. One of the two recommendations must be from the applicant’s dissertation advisor.
• Postdoctoral Research Leave Fellowship applicants: Provide two recommendations from the applicant’s advisers, colleagues or others well acquainted with their project or work.
• Short-Term Research Publication Grant applicants: Provide two recommendations from the applicant’s advisers, colleagues or others well acquainted with the applicant, their project/work and their teaching.
• Dissertation Fellowship applicants: Submit transcripts for all graduate work and courses listed in the application. Transcripts must show grades for coursework transferred in. If the transcript shows transfer courses and credits without grades, a transcript from the institution where the courses were taken is required. If you studied at an institution that does not require coursework or provide transcripts, an institutional letter stating that is required.
• Postdoctoral Research Leave Fellowship and Short-Term Publication Grant applicants: Proof of degree: Submit transcript(s)** or original letter showing proof of a Ph.D., Ed.D., M.F.A., J.D., M.D., D.M.D., D.V.M., D.B.A., D.S.W., or M.P.H. degree.
• Dissertation Fellowship applicants: Dissertation certification form: Submit the form verifying the completion of all required coursework and qualifying examinations for the doctorate and approval of your dissertation research proposal (plan of research) signed by an institutional officer. No substitutions for this form will be accepted.
• Dissertation applicants: If you will conduct your project at an institution other than your own during the fellowship year, submit the form that indicates you have approval from the institution and the authority with whom the work will be done to conduct the research, laboratory or office space, and library privileges during the fellowship year. No substitutions for this form will be accepted. If you will conduct your project at your home institution, no project institution form is needed.
• Postdoctoral Research Leave Fellowship and Short-Term Publication Grant applicants: Submit the form that indicates you have approval from the proposed institution and the authority with whom the work will be done to conduct the research and have institutional affiliation, laboratory or office space, and library privileges during the fellowship year. No substitutions for this form will be accepted.

*A certified English translation is required for all components provided in a foreign language. Translations must bear a mark of certification or official signature that the translation is true and complete.

**All transcripts provided must include the applicant’s full name, the school’s name, all courses and all grades, as well as any other information requested in in the application instructions.  

See More Fellowship and Grant Opportunities

For questions or technical support from ISTS, our technical consultant, please email [email protected] . Enter AAUW-AF if the website prompts you for a program key. We encourage applicants not to opt out of communications from ISTS, to ensure you receive important communications from AAUW.  

Meet a Recent American Fellow

Sarah Biscarra Dilley ’s research is focused on matrifocal and gender-expansive governance from northern villages of yak titʸu titʸu yak tiłhini to Mokupuni o Hawai‘i, rooted in shared land and kinship-based epistemology. Her written, visual and material practice is grounded in collaboration across experiences, peoples and place, connecting extractive industries, absent treaties and enclosure to emphasize movement, embodied protocol and possibility. Her aspirations are toward cultural resurgence and the return of land to her families’ stewardship.

Our Alumnae

Ayana Johnson

2010–11 American Fellow and marine biologist, policy expert and conservation strategist. She is the founder and CEO of Ocean Collectiv and founder of Urban Ocean Lab.

Melissa Harris-Perry

2001-02 AAUW American Fellow and Maya Angelou Presidential Chair at Wake Forest University, a columnist for the Nation, editor-at-large for ZORA, author of Sister Citizen: Shame, Stereotypes, and Black Women in America , and former host of The Melissa Harris-Perry Show on MSNBC.

Kimberly Ennico-Smith

1997-98 AAUW American Fellow and staff scientist with NASA who served as deputy project scientist for NASA’s New Horizons Mission, the historic project responsible for capturing unprecedented photos of Pluto.

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Pakistani students’ perceptions about knowledge, use and impact of artificial intelligence (AI) on academic writing: a case study

• Published: 11 September 2024

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• Shaista Rashid 1 ,
• Sadia Malik   ORCID: orcid.org/0000-0002-4989-2359 2 ,
• Faheem Abbas 2 &
• Javaria Ahmad Khan 3  

Integrating artificial intelligence (AI) in language pedagogy can help learn and develop many skills. In this context, this study explores Pakistani students' perceptions and trends regarding the knowledge, use, and impact of AI on their academic writing. The data was collected using a quantitative method, using a questionnaire through cluster sampling of four faculties and random sampling of 229 students from Bahuddin Zakariya University, Multan, Pakistan. Data is subjected to frequency analysis, Kruskal–Wallis hypothesis test, and chi-square association test using SPSS. The findings reveal that most students agree regarding the knowledge, use, and impact of AI on their academic writing. For the Kruskal–Wallis test, significant variations are seen in semesters and age groups for all three variables; however, only the knowledge variable shows significant variation across faculties. Moreover, chi-square test results indicate an association among components of knowledge, use, and impact of AI. The research suggests that academia should introduce AI as a pedagogical tool to improve students' comprehension, productivity, and writing quality. Furthermore, trends indicate that comprehensive policy formulation should be implemented to equip students of all faculties, semesters, and age groups to use technology equally.

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Ahmed, M., Siddiqui, M., & Usman, T. (2024). Impact of artificial intelligence-based writing assistant on the academic writing skills of university faculty in Pakistan. International Journal of Human and Society, 4 (1), 539–545.

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Continuous enhancement of mobile devices such as smartphones offer new opportunities for using these technologies in inquiry-based learning environments. Inquiry-based learning has followed deductive and inductive forms of inquiry, while the abductive form of inquiry that target the development of higher-order thinking skills such as critical thinking are less prevalent. This study investigated the use of mobile technology in abductive-inquiry based teaching and learning of chemical bonding for grade 11 physical sciences learners in two South African schools. The study employed an explanatory sequential mixed-methods design that entailed first collecting quantitative data and then qualitative data to help explain or elaborate on the quantitative results. Two grade 11 Physical Sciences classes were randomly designated as the experimental and control groups in each of the two different schools. The experimental group in each school experienced activities in a laboratory using mobile technology-enhanced abductive scientific inquiry through the ‘Molecular Workbench’ web-based simulation using a mobile device, while the control group in each school experienced activities in abductive scientific inquiry in a science laboratory without using mobile learning technology. The principal findings indicated that learners within the control group displayed a significant increase in their performance to create a scientifically accurate hypothesis that is the essence of abductive inquiry, whereas for the experimental group there was no significant improvement in their hypothesis generation capacity. However, participants within the experimental group felt that their use of mobile devices created a sense of learner agency amongst themselves, developed their communication skills, made them feel responsible for their own learning, and also made learning scientific concepts more fun as opposed to what they are normally exposed to.

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UC Davis Exceeds $1 Billion in Research Awards for Third Consecutive Year

• by Neelanjana Gautam
• September 09, 2024

The University of California, Davis, once again exceeded $1 billion in new external research awards in the fiscal year 2023-24, surpassing the amount received in the previous year by $33 million. The funding, which totaled $1.039 billion, will enable scientific discoveries and innovations addressing a wide range of global needs.    

“For the third consecutive year, UC Davis has set the bar high for research funding,” said Chancellor Gary S. May. “This investment will empower our university to continue transformative research confronting the world’s most persistent challenges from climate change to child welfare, generating unprecedented opportunities for individuals both here in California and across the globe."

Vice Chancellor for Research Simon Atkinson added, “UC Davis’ success in attaining this level of funding and delivering global impact is a testament to the broad ingenuity and passion of our faculty, researchers and staff throughout the university.”

The federal government continued to be the largest provider of funding at $441 million. The second leading source was the state of California at $237 million, up by $76 million. Funding from industry made up the third highest source, totaling $95 million. 

Among the colleges and schools, the top three recipients were the School of Medicine ($400 million), the College of Agricultural and Environmental Sciences ($169 million) and the College of Engineering ($118 million). 

The largest single award, $52.39 million went to Alison Book , executive director of UC Davis Human Services at UC Davis Continuing and Professional Education , for a program to fortify the future of the child welfare workforce throughout California (described further below).  The award came from the California Department of Social Services, with funding from U.S. Department of Health and Human Services Administration for Children and Families.

Research at UC Davis also produces innovations that become the foundation for tomorrow’s products, services and businesses. Last fiscal year, researchers generated 140 records of invention, submitted 208 patent applications and secured 109 patents and plant certificates.

To learn more about the impact of UC Davis research and innovation, view our Annual Research Impact Report .

Notable awards by college and school

College of agricultural and environmental sciences, building resilient and equitable urban stream corridors .

Natural hazards, socio-economic wellbeing and ecological functions all intertwine in California’s urban stream corridors, as these pathways are heavily relied on for flood and pollution control, recreation, ecosystem services, education and residency. Led by Professor Gregory Pasternack , a multi-institutional and interdisciplinary team will use a combination of data collection in local communities and their streams, model-predicted future climate condition maps, remote sensing and spatial analysis to understand landscape patterns that amplify risks and identify locations where nature-based solutions can be most effective. The project is funded by the University of California Office of the President.  

College of Biological Sciences 

Ecology shaping cognition: an exploration with wild bees .

Felicity Muth , an assistant professor in the Department of Neurobiology, Physiology, and Behavior, is leading a research project focused on understanding how environments shape cognition in bumblebees and other bee species. Funded by The National Geographic Society as part of their Wildlife Intelligence Project , the project will investigate cognitive differences between queen and forager bumblebees, as well as the impact of dietary generalism on bees' cognitive abilities. Muth’s team will study bees in natural environments to determine how they learn and solve problems while foraging, on public land in Nevada and California, contributing to a broader understanding of animal behavior and cognition in the wild.

College of Engineering

Developing high-resolution imaging capabilities for advancing bio-informed flight research .

Christina Harvey , an assistant professor of mechanical and aerospace engineering at UC Davis and Michelle Hawkins , a professor in the School of Veterinary Medicine and director of the California Raptor Center , are launching the bird flight research center with funding from the Department of Defense. The new center will utilize motion capture and photogrammetry — which uses photography to determine the distance between objects — technologies to image birds in flight and create 3D models of the wing shapes to inform the design and capabilities of the next generation of uncrewed aerial systems. The center will be the first of its kind in the country. Harvey has been studying how bird flight can be used to improve aircraft design, and was awarded the Packard Fellowship in Science and Engineering from the David and Lucile Packard Foundation last year. 

College of Letters and Science

Understanding gender-based differences in responses to social stress .

Stress-related mental illnesses are more common in women than men, and these differences arise after puberty when hormones shape brain development. Surprisingly little is known about how adolescent brain development affects behavior. Led by  Brian Trainor , a professor in the Department of Psychology, the team will examine how  sexual differentiation during puberty shapes stress-sensitive neural circuits  that control social behavior. A team of scientists has found that testosterone is the key hormone that drives differences in responses to social stress in male and female mice. Funded by the National Institutes of Mental Health, the team will test the hypothesis that activation of the androgen receptor during puberty alters gene expression in the brain to permanently program neural circuits of social behavior to be less sensitive to social stress. 

Continuing and Professional Education

Improving quality of child welfare services .

The California Title IV-E education program provides professional education and monetary support to undergraduate and graduate social work students who intend to pursue or continue a career in the field of public child welfare. Through funding awarded by the California Department of Social Services, a team led by Alison Book , principal investigator and executive director of Human Services at UC Davis Continuing and Professional Education , will partner with UCLA to administer the Title IV-E program and its sub-awards at California’s 20 universities with social work programs. The new partnership will provide more opportunities to fortify the professional preparedness that participating students receive, ultimately strengthening the quality of child welfare services provided to families throughout the state.

Graduate School of Management

Enhancing open-source software use for optimization .

Professor and Associate Dean David Woodruff is working with researchers at Lawrence Livermore National Laboratory to enhance open-source software used for optimization when the input data are uncertain. Although the enhancements are general, one target is to be able to optimize large economic models of electricity generation and delivery infrastructure. Another target is to be able to optimize electric grid planning, management and operation. This work also involves collaboration with the U.S. Department of Energy National Renewable Energy Laboratory . 

School of Education

Creating professional learning opportunities for bilingual teachers and caregivers to teach english language learners .

Dual language education is an extremely effective way to teach students identified as  English language learners, but there are not enough qualified, credentialed bilingual teachers to meet the rising need for bilingual education across the country. A team led by Associate Professor of Teaching in Education Claudia Rodriguez-Mojica is developing and implementing workshops in Spanish for bilingual teachers and caregivers of English language learners in California and New Mexico, states which have among the highest number of K-8 students in the country who are identified as English language learners. The workshops will provide a professional learning environment in Spanish so that participating teachers and caregivers can gain and refine the skills they need to teach in K-8 dual language classrooms. The project is funded by a grant from the U.S. Department of Education’s Office of English Language Acquisition.

School of Law

Providing sustainable groundwater management for underrepresented communities .

The Small Farmer Clinic , an extension of the law school’s Aoki Water Justice Clinic  will provide legal assistance to family farmers regulated by California’s Sustainable Groundwater Management Act. The clinic was created through a Water Foundation grant that UC Davis Law Senior Assistant Dean for Administration Brett Burns , Aoki Water Justice Clinic Director Rob Mullaney and Professor and Director of Clinical Legal Education Gabriel “Jack” Chin were instrumental in securing. David Sandino is the new director of the clinic.

School of Medicine

Preparing californians for public health emergencies .

The UC Davis School of Medicine Department of Public Health Sciences assists with providing qualified personnel to aid the Emergency Preparedness Office and other California Department of Public Health programs with planning and activities to prepare Californians for public health emergencies. This includes drills and exercise scenarios, planning for the strategic national stockpile, crisis response, statewide disaster planning, and developing publications and communications to support the California Department of Public Health programs. The project is led by  Bradley Pollock , chair and distinguished professor of the Department of Public Health Sciences and funded by the California Department of Public Health. 

School of Nursing

Funding to help counter national downward trend of graduate student enrollment in nursing.

Enrollment in Doctor of Philosophy nursing programs across the nation is down more than 3% in the last year, posing a threat to meeting the nation’s health care needs, according to a survey from the American Association of Colleges of Nursing . New investment from the Gordon and Betty Moore Foundation aims to reverse that downward trend. It offers to lower the cost for students in the Doctor of Philosophy (Ph.D.) in Nursing Science and Health-Care Leadership program at the Betty Irene Moore School of Nursing at UC Davis. Led by Dean Stephen Cavanagh , the funding will provide substantial tuition packages for up to five years of full-time study, and also cover travel for conference presentations, start-up awards, research-related expenses and dissertation assistance. 

School of Veterinary Medicine

Combating rift valley fever  .

This One Health project will continue the development of DDVax, a vaccine candidate for Rift Valley fever relevant for people and livestock. This high-consequence pathogen is found throughout Africa and parts of the Middle East and the risk of introduction into Europe, Asia, and the Americas is very high. The project , led by Brian Bird , director of the One Health Institute Laboratory and professor of emerging and zoonotic diseases, will enable a novel human vaccine trial against Rift Valley fever virus in Africa. Funded by the Coalition for Epidemic Preparedness Innovations and the European Union’s Horizon Europe program, the project aims to work with partners in African countries affected by the virus to assess the safety and immune response of the vaccine in people most at risk. 

Office of Research, interdisciplinary research awards

Interdisciplinary research units within the Office of Research continue to attract significant funding by bringing experts together from different fields of study. These joint efforts aim to address complex, large-scale challenges that require expertise from many perspectives. Notable examples include:

Growing GRASS and climate-smart value-added US commodity markets

Led by  Patrick Huber , director of the Center for Conservation Informatics in the  Institute of the Environment  and funded by the U.S. Department of Agriculture and the American Sustainable Business Institute, Inc., the project aims to define successful regenerative practices and to quantify greenhouse gas sequestration while bringing beef and bison byproducts into sustainable supply chains.

Remote monitoring for equity in advancing control of hypertension (REACH) 

Nearly  116 million Americans  have hypertension, the most common cause of avoidable death and disability in the U.S. It is of particular importance to conduct studies of home blood pressure monitoring among communities most impacted by hypertension, given that Black and Latino communities, those with low income, and those with limited proficiency in English are more likely to be diagnosed with hypertension or have uncontrolled hypertension. Led by  Courtney Lyles , director of the Center for Healthcare Policy and Research and professor in the UC Davis School of Medicine’s Department of Public Health Sciences, the  REACH project  will test patient support for home blood pressure monitoring, with the goal of varying clinical pharmacist outreach and support. REACH is funded by the National Institutes of Health and the National Institute of Heart, Lung and Blood Institute, and 540 patients will be enrolled. The project is in partnership with UC San Francisco.  

Measuring charging infrastructure reliability in California 

The reliability of the public charging infrastructure in California is critical to facilitating the adoption and utilization of electric vehicles in order to meet the state’s zero-emission vehicle sales and emissions reductions goals. Led by Gil Tal, director of the STEPS+ and EV Research Center at the Institute of Transportation Studies , this project aims to investigate and understand the difference in reliability between two sources: the EV service provider and the customer, the cause of any reliability or charging failures, and provide potential solutions for providers to maximize the reliability of chargers. This project will be focused on Direct Current Fast Chargers in California and will separately explore the charging reliability experience in different communities including urban, rural, low-income and disadvantaged communities. The project is funded by the California Energy Commission. 

Reports are based on the principal investigator’s home school or college. Where funds are awarded up-front to cover several years, the money is counted in the first year the award was received. Incrementally funded awards are counted as authorized in each year. Funding amounts reflect a correction in the attribution of a $3 million award to the School of Nursing that was previously assigned to the School of Medicine. 

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