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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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What is a hypothesis? 

What method states that a hypothesis needs to be formulated to produce good research?

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What are the requirements of a good hypothesis? 

What steps do researchers need to take when formulating a testable hypothesis? 

What is a hypothesis predicting? 

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

Why are hypotheses needed in research? 

What is an operationalised variable?

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• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 18

The hypothesis is a predictive, testable statement concerning the outcome/ results the researcher expects to find. 

The scientific method states that researchers need to formulate a good hypothesis before starting the research. 

Directional, alternative hypothesis 

Alternative hypothesis 

A good hypothesis should:

• identify and operationalise the independent and dependent variable
• be testable 
• be falsifiable 
• predictive statements 

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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What Is Formulation of Hypothesis? A Beginner's Introduction

Formulating a hypothesis is a key part of the scientific process. It helps researchers define what they are looking to discover, identify the variables involved, and plan how to analyze the data. A good hypothesis is clear, testable, and based on what is already known in the field.

Key Takeaways

• A hypothesis is a statement that predicts the relationship between variables.
• Formulating a hypothesis helps to clarify the research question.
• A well-formulated hypothesis is specific and testable.
• Reviewing existing literature is crucial for grounding your hypothesis.
• Different fields may have unique approaches to hypothesis formulation.

Understanding the Concept of Hypothesis Formulation

Definition and importance.

A hypothesis is a statement that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. Formulating a hypothesis is a critical step in the hypothesis testing process. It helps to clarify the research question, identify the variables, and guide the data analysis. A well-formulated hypothesis is testable, specific, and based on previous research in the area.

Historical Background

The concept of hypothesis formulation has evolved over centuries. Early scientists like Galileo and Newton used hypotheses to explain natural phenomena. In modern times, the scientific method has formalized the process, making it a cornerstone of research across various fields. Understanding the historical context helps appreciate the importance of clear, testable statements in research.

Basic Principles

Formulating a hypothesis involves several key principles:

• Identifying the Problem: Clearly define the research problem you aim to address.
• Reviewing Literature: Conduct a thorough literature review to understand existing knowledge and gaps.
• Developing a Research Question: Frame a research question that is specific and testable.
• Formulating the Hypothesis: Develop a hypothesis that is grounded in existing knowledge and can be tested through data collection and analysis.

By following these principles, you can develop a hypothesis that is both specific and testable, ensuring a solid foundation for your research.

Steps Involved in Formulating a Hypothesis

Formulating a hypothesis is a crucial part of the research process. It involves several steps that help you develop a hypothesis that is specific, testable, and grounded in existing knowledge. By following these steps, you can ensure that your hypothesis is well-constructed and ready for testing.

Identifying the Research Problem

The first step in formulating a hypothesis is to identify the research problem . You need to define the problem clearly and precisely so that you can develop a hypothesis that is specific and testable. For example, if you are interested in studying the effect of caffeine on memory, you need to clearly define what you mean by memory and what kind of memory you are interested in.

Conducting a Literature Review

Once you have identified the research problem, the next step is to conduct a literature review. This involves reviewing existing research on the topic to understand what is already known and what gaps exist in the current knowledge. By conducting a thorough literature review, you can ensure that your hypothesis is grounded in existing knowledge and addresses a gap in the research.

Developing a Research Question

After conducting a literature review , the next step is to develop a research question. This involves formulating a question that your research will aim to answer. The research question should be specific, clear, and focused on the research problem you have identified. A well-formulated research question will guide the development of your hypothesis and the design of your study.

Types of Hypotheses in Research

In research, hypotheses come in various forms, each serving a unique purpose. Understanding these types is crucial for designing effective studies and drawing meaningful conclusions.

Null Hypothesis

The null hypothesis, often denoted as H0, proposes that there is no significant relationship between the variables being studied. It serves as a baseline that researchers aim to test against. For instance, in a study on the effects of a new drug, the null hypothesis might state that the drug has no effect on patient outcomes.

Alternative Hypothesis

The alternative hypothesis, denoted as Ha, suggests that there is a significant relationship between the variables. This is what researchers hope to prove. For example, if you are studying the impact of a teaching method on student performance, the alternative hypothesis might propose that the new method improves performance.

Directional and Non-Directional Hypotheses

Directional hypotheses predict the direction of the expected outcome. For example, "Eating breakfast will improve students' test scores." This type of hypothesis specifies that one variable will lead to an increase or decrease in another. Non-directional hypotheses, on the other hand, do not predict the direction but merely state that a relationship exists. For example, "There is a relationship between eating breakfast and students' test scores."

Understanding these different types of hypotheses is essential for any research endeavor. They help in structuring the study and in making sense of the results. As noted in research methods in palliative care , the complexity of research often requires a nuanced approach to hypothesis formulation. Additionally, as highlighted in statistical storytelling , the importance of hypothesis testing cannot be overstated in drawing valid conclusions from data.

Characteristics of a Well-Formulated Hypothesis

A well-formulated hypothesis is essential for guiding your research and ensuring that your study is on the right track. Testability is a key characteristic of a good hypothesis. This means that the hypothesis can be supported or refuted through empirical evidence. If a hypothesis cannot be tested, it is not useful for scientific inquiry.

Another important trait is specificity . A hypothesis should clearly define the variables and the expected relationship between them. This clarity helps in designing experiments and observations, making the research process more straightforward.

Lastly, a well-formulated hypothesis should be grounded in existing knowledge . This means it should be based on previous research and theories in the field. By building on what is already known, you can create a hypothesis that is both relevant and meaningful.

Role of Variables in Hypothesis Formulation

In hypothesis formulation, understanding the role of variables is crucial. Variables are the elements you measure or manipulate in your research . They help in explaining any cause and effect in your study. Proper identification and operationalization of these variables ensure consistency and replicability in your research.

Independent Variables

Independent variables are the factors you control or manipulate in your study. They are the presumed cause in a cause-and-effect relationship. For example, if you are studying the effect of sunlight on plant growth, the amount of sunlight is the independent variable.

Dependent Variables

Dependent variables are the outcomes you measure in your research. They are the presumed effect in a cause-and-effect relationship. Continuing with the previous example, the growth of the plant would be the dependent variable.

Control Variables

Control variables are factors that are kept constant to prevent them from influencing the outcome of the experiment. By controlling these variables, you can ensure that the results are due to the manipulation of the independent variable alone. This is essential for the reliability of your research.

Common Pitfalls in Hypothesis Formulation

When formulating a hypothesis, there are several common pitfalls that you should be aware of to ensure your research is on the right track. Avoiding these mistakes can significantly improve the quality of your hypothesis and, consequently, your research outcomes.

Formulating Hypotheses in Different Fields

When you formulate hypotheses, the approach can vary greatly depending on the field of study. Each discipline has its own set of norms and expectations that guide the process. Understanding these differences is crucial for crafting effective hypotheses that are both testable and relevant to your research area.

Testing the Hypothesis: An Overview

Data collection methods.

When testing a hypothesis, the first step is to gather data. This can be done through various methods such as surveys, experiments, or observational studies. Choosing the right data collection method is crucial because it impacts the reliability of your results. For instance, if you are studying human behavior, surveys and interviews might be more appropriate than observational studies.

Statistical Analysis

Once you have collected your data, the next step is to analyze it statistically. This involves using mathematical tools to determine whether your data supports your hypothesis. Common statistical tests include t-tests, chi-square tests, and ANOVA. The choice of test depends on the type of data you have and the nature of your hypothesis. Statistical analysis helps you draw meaningful conclusions from your data.

Interpreting Results

After performing statistical analysis, you need to interpret the results. This means understanding what the numbers are telling you about your hypothesis. Are the results significant? Do they support or refute your hypothesis? Interpretation is key to making informed decisions based on your research. Remember, even if your hypothesis is not supported, the findings are still valuable as they contribute to the broader field of knowledge.

Theoretical Frameworks and Hypothesis Formulation

Understanding the role of theory in research is crucial for any researcher. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered. This framework helps in identifying the research gap and provides a basis for formulating your hypothesis.

Building a theoretical framework involves several steps. First, you need to conduct a thorough literature review to understand existing theories and how they relate to your research question. Next, you should identify the key variables and their relationships based on these theories. Finally, you integrate these elements to form a coherent framework that guides your hypothesis formulation .

Integrating hypotheses with theory is essential for a robust research proposal. Your hypothesis should be grounded in existing knowledge and should aim to test the relationships between variables as suggested by your theoretical framework. This approach not only strengthens your research design but also ensures that your study contributes to the broader field of knowledge.

Practical Tips for Beginners in Hypothesis Formulation

Reviewing existing literature.

Before you start formulating your hypothesis, it's essential to review existing literature . This helps you understand what has already been studied and where there might be gaps in the research. By doing this, you can ensure your hypothesis is grounded in existing knowledge and not duplicating previous work.

Consulting Experts

Another valuable step is to consult experts in the field. They can provide insights and feedback that you might not have considered. This can be especially helpful in refining your hypothesis and making sure it is both specific and testable .

Iterative Refinement

Formulating a hypothesis is not a one-time task. It often requires iterative refinement . Start with a broad idea and then narrow it down. This process helps in making your hypothesis clear and focused. Remember, a well-formulated hypothesis should be clear and specific , as highlighted in best practices: from research to hypothesis creation .

By following these steps, you can develop a hypothesis that is specific, testable, and based on previous research in the area.

Case Studies: Successful Hypothesis Formulation

Classic examples in science.

Understanding the importance of a strong thesis statement is crucial in academic writing. One of the most famous examples is Gregor Mendel's work on pea plants. Mendel formulated hypotheses about inheritance patterns, which he then tested through meticulous experiments. His clear, specific, and supported thesis statement laid the foundation for modern genetics.

Modern Research Case Studies

In recent years, the consulting hypothesis tree has become a valuable problem-solving tool. For instance, in a study on climate change, researchers developed a hypothesis tree to explore various factors affecting global temperatures. This structured approach helped them identify key variables and test their hypotheses effectively.

Lessons Learned

Crafting a bachelor thesis involves several steps, including formulating a clear research question. A well-defined hypothesis is essential for guiding your research and ensuring that your findings are meaningful. By following a structured approach, you can avoid common pitfalls and achieve successful results.

In our "Case Studies: Successful Hypothesis Formulation" section, we dive into real-world examples of how students have effectively crafted their hypotheses. These stories highlight the practical steps and strategies that led to their success. If you're looking to master your thesis writing, don't miss out on our comprehensive guides and resources. Visit our website to learn more and take the first step towards a stress-free thesis journey.

In summary, formulating a hypothesis is a vital part of the scientific method and research process. It serves as a foundation for your study, helping to define the problem, guide data collection, and shape the analysis. A well-crafted hypothesis is clear, specific, and testable, making it easier to draw meaningful conclusions from your research. By taking the time to develop a strong hypothesis, you set the stage for a successful investigation, ensuring that your efforts are focused and grounded in existing knowledge. Remember, a good hypothesis not only answers your research question but also opens the door to further inquiry and discovery.

Frequently Asked Questions

What is a hypothesis.

A hypothesis is a guess or prediction about how things work. It's a statement that can be tested to see if it's true.

Why is formulating a hypothesis important?

Formulating a hypothesis helps you focus your research, identify key variables, and guide your data analysis.

What are the steps to formulate a hypothesis?

First, identify the research problem. Then, review existing literature, and finally, develop a research question that leads to your hypothesis.

What is a null hypothesis?

A null hypothesis is a type of hypothesis that states there is no relationship between the variables being studied.

What makes a hypothesis well-formulated?

A good hypothesis is testable, specific, and based on existing knowledge.

What are independent and dependent variables?

Independent variables are the ones you change to see if they affect something else. Dependent variables are what you measure to see if they change because of the independent variables.

What are common mistakes in formulating a hypothesis?

Common mistakes include making the hypothesis too broad, not being clear, and ignoring previous research.

Can you give an example of a hypothesis in market research?

Sure! For example, a hypothesis in market research could be: 'If we lower the price of our product, sales will increase.'

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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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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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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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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.

Learn about our Editorial Process

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.

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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."

How to Write a Hypothesis in 6 Steps, With Examples

A hypothesis is a statement that explains the predictions and reasoning of your research—an “educated guess” about how your scientific experiments will end. As a fundamental part of the scientific method, a good hypothesis is carefully written, but even the simplest ones can be difficult to put into words. 

Want to know how to write a hypothesis for your academic paper ? Below we explain the different types of hypotheses, what a good hypothesis requires, the steps to write your own, and plenty of examples.

Write with confidence Grammarly helps you polish your academic writing Write with Grammarly  

What is a hypothesis? 

One of our 10 essential words for university success , a hypothesis is one of the earliest stages of the scientific method. It’s essentially an educated guess—based on observations—of what the results of your experiment or research will be. 

Some hypothesis examples include:

• If I water plants daily they will grow faster.
• Adults can more accurately guess the temperature than children can. 
• Butterflies prefer white flowers to orange ones.

If you’ve noticed that watering your plants every day makes them grow faster, your hypothesis might be “plants grow better with regular watering.” From there, you can begin experiments to test your hypothesis; in this example, you might set aside two plants, water one but not the other, and then record the results to see the differences. 

The language of hypotheses always discusses variables , or the elements that you’re testing. Variables can be objects, events, concepts, etc.—whatever is observable. 

There are two types of variables: independent and dependent. Independent variables are the ones that you change for your experiment, whereas dependent variables are the ones that you can only observe. In the above example, our independent variable is how often we water the plants and the dependent variable is how well they grow. 

Hypotheses determine the direction and organization of your subsequent research methods, and that makes them a big part of writing a research paper . Ultimately the reader wants to know whether your hypothesis was proven true or false, so it must be written clearly in the introduction and/or abstract of your paper. 

7 examples of hypotheses

Depending on the nature of your research and what you expect to find, your hypothesis will fall into one or more of the seven main categories. Keep in mind that these categories are not exclusive, so the same hypothesis might qualify as several different types. 

1 Simple hypothesis

A simple hypothesis suggests only the relationship between two variables: one independent and one dependent. 

• If you stay up late, then you feel tired the next day. 
• Turning off your phone makes it charge faster. 

2 Complex hypothesis

A complex hypothesis suggests the relationship between more than two variables, for example, two independents and one dependent, or vice versa. 

• People who both (1) eat a lot of fatty foods and (2) have a family history of health problems are more likely to develop heart diseases. 
• Older people who live in rural areas are happier than younger people who live in rural areas. 

3 Null hypothesis

A null hypothesis, abbreviated as H 0 , suggests that there is no relationship between variables. 

• There is no difference in plant growth when using either bottled water or tap water. 
• Professional psychics do not win the lottery more than other people. 

4 Alternative hypothesis

An alternative hypothesis, abbreviated as H 1 or H A , is used in conjunction with a null hypothesis. It states the opposite of the null hypothesis, so that one and only one must be true. 

• Plants grow better with bottled water than tap water. 
• Professional psychics win the lottery more than other people. 

5 Logical hypothesis

A logical hypothesis suggests a relationship between variables without actual evidence. Claims are instead based on reasoning or deduction, but lack actual data.  

• An alien raised on Venus would have trouble breathing in Earth’s atmosphere. 
• Dinosaurs with sharp, pointed teeth were probably carnivores. 

6 Empirical hypothesis

An empirical hypothesis, also known as a “working hypothesis,” is one that is currently being tested. Unlike logical hypotheses, empirical hypotheses rely on concrete data. 

• Customers at restaurants will tip the same even if the wait staff’s base salary is raised. 
• Washing your hands every hour can reduce the frequency of illness. 

7 Statistical hypothesis

A statistical hypothesis is when you test only a sample of a population and then apply statistical evidence to the results to draw a conclusion about the entire population. Instead of testing everything , you test only a portion and generalize the rest based on preexisting data. 

• In humans, the birth-gender ratio of males to females is 1.05 to 1.00.  
• Approximately 2% of the world population has natural red hair. 

What makes a good hypothesis?

No matter what you’re testing, a good hypothesis is written according to the same guidelines. In particular, keep these five characteristics in mind: 

Cause and effect

Hypotheses always include a cause-and-effect relationship where one variable causes another to change (or not change if you’re using a null hypothesis). This can best be reflected as an if-then statement: If one variable occurs, then another variable changes. 

Testable prediction

Most hypotheses are designed to be tested (with the exception of logical hypotheses). Before committing to a hypothesis, make sure you’re actually able to conduct experiments on it. Choose a testable hypothesis with an independent variable that you have absolute control over. 

Independent and dependent variables

Define your variables in your hypothesis so your readers understand the big picture. You don’t have to specifically say which ones are independent and dependent variables, but you definitely want to mention them all. 

Candid language

Writing can easily get convoluted, so make sure your hypothesis remains as simple and clear as possible. Readers use your hypothesis as a contextual pillar to unify your entire paper, so there should be no confusion or ambiguity. If you’re unsure about your phrasing, try reading your hypothesis to a friend to see if they understand. 

Adherence to ethics

It’s not always about what you can test, but what you should test. Avoid hypotheses that require questionable or taboo experiments to keep ethics (and therefore, credibility) intact.

How to write a hypothesis in 6 steps

1 ask a question.

Curiosity has inspired some of history’s greatest scientific achievements, so a good place to start is to ask yourself questions about the world around you. Why are things the way they are? What causes the factors you see around you? If you can, choose a research topic that you’re interested in so your curiosity comes naturally. 

2 Conduct preliminary research

Next, collect some background information on your topic. How much background information you need depends on what you’re attempting. It could require reading several books, or it could be as simple as performing a web search for a quick answer. You don’t necessarily have to prove or disprove your hypothesis at this stage; rather, collect only what you need to prove or disprove it yourself. 

3 Define your variables

Once you have an idea of what your hypothesis will be, select which variables are independent and which are dependent. Remember that independent variables can only be factors that you have absolute control over, so consider the limits of your experiment before finalizing your hypothesis. 

4 Phrase it as an if-then statement

When writing a hypothesis, it helps to phrase it using an if-then format, such as, “ If I water a plant every day, then it will grow better.” This format can get tricky when dealing with multiple variables, but in general, it’s a reliable method for expressing the cause-and-effect relationship you’re testing. 

5  Collect data to support your hypothesis

A hypothesis is merely a means to an end. The priority of any scientific research is the conclusion. Once you have your hypothesis laid out and your variables chosen, you can then begin your experiments. Ideally, you’ll collect data to support your hypothesis, but don’t worry if your research ends up proving it wrong—that’s all part of the scientific method. 

6 Write with confidence

Last, you’ll want to record your findings in a research paper for others to see. This requires a bit of writing know-how, quite a different skill set than conducting experiments. 

That’s where Grammarly can be a major help; our writing suggestions point out not only grammar and spelling mistakes , but also new word choices and better phrasing. While you write, Grammarly automatically recommends optimal language and highlights areas where readers might get confused, ensuring that your hypothesis—and your final paper—are clear and polished.

Have a language expert improve your writing

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• Knowledge Base

Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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.

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Research bias

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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 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).

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.

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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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What is a hypothesis? 

What method states that a hypothesis needs to be formulated to produce good research?

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What are the requirements of a good hypothesis? 

What steps do researchers need to take when formulating a testable hypothesis? 

What is a hypothesis predicting? 

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

Why are hypotheses needed in research? 

What is an operationalised variable?

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• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 18

The hypothesis is a predictive, testable statement concerning the outcome/ results the researcher expects to find. 

The scientific method states that researchers need to formulate a good hypothesis before starting the research. 

Directional, alternative hypothesis 

Alternative hypothesis 

A good hypothesis should:

• identify and operationalise the independent and dependent variable
• be testable 
• be falsifiable 
• predictive statements 

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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Formulating and Testing Hypotheses

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• Gary A. Wobeser 2  

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The term hypothesis has been mentioned several times in the preceding chapters. The definition that will be used here is that a hypothesis is a proposition set forth as explanation for the occurrence of a specified phenomenon. The basis of scientific investigation is the collection of information that is used either to formulate or to test hypotheses. One assesses the important variables and tries to build a model or hypothesis that explains the observed phenomenon. In general, a hypothesis is formulated by rephrasing the objective of a study as a statement, e.g., if the objective of an investigation is to determine if a pesticide is safe, the resulting hypothesis might be “ the pesticide is not safe ”, or alternatively that “ the pesticide is safe ”. A hypothesis is a statistical hypothesis only if it is stated in terms related to the distribution of populations. The general hypothesis above might be refined to: “ this pesticide, when used as directed, has no effect on the average number of robins in an area ”, which is a testable hypothesis. The hypothesis to be tested is called the null hypothesis (H 0 ). The alternative hypothesis (H 1 ) for the above example would be “ this pesticide, when used as directed, has an effect on the average number of robins in an area”. In testing a hypothesis, H 0 is considered to be true, unless the sample data indicate otherwise, (i.e., that the pesticide is innocent, unless proven guilty). Testing cannot prove H 0 to be true but the results can cause it to be rejected. In accepting or rejecting H 0 , two types of error may be made. If H 0 is rejected when, in fact, it is true a type 1 error has been committed. If Ho is not true and the test fails to reject it, a type 2 error has been made.

“ Research in the field, through study of disease as it manifests itself in nature, is an important and independent approach to solution of medical problems. Modern medical progress has been so thoroughly associated with research in the biological laboratory, and it has been so largely a development of the experimental method, that this other and older method has come in recent years to be overshadowed ” (Gordon, 1950)

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Wobeser, G.A. (1994). Formulating and Testing Hypotheses. In: Investigation and Management of Disease in Wild Animals. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5609-8_6

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Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.