importance of assumptions and hypothesis in research

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Step 1. Ask a question

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved July 4, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

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

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

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

What is a Research Hypothesis?

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

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

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

Importance of Hypothesis in Research

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

• A research hypothesis helps test theories.

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

• It serves as a great platform for investigation activities.

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

• Hypothesis guides the research work or study.

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

• Hypothesis sometimes suggests theories.

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

• It helps in knowing the data needs.

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

• The hypothesis explains social phenomena.

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

• Hypothesis provides a relationship between phenomena for empirical Testing.

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

• It helps in knowing the most suitable analysis technique.

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

Characteristics of a Good Research Hypothesis

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

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

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

Types of Research Hypotheses

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

01. Null Hypothesis

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

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

02. Alternative Hypothesis

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

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

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

03. Directional Hypothesis

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

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

04. Non-directional Hypothesis

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

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

05. Simple Hypothesis

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

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

06. Complex Hypothesis

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

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

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

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

07. Associative Hypothesis

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

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

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

08. Causal Hypothesis

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

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

09. Empirical Hypothesis

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

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

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

10. Statistical Hypothesis

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

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

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

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

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

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

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

Step 2: Conduct a literature review

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

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

Step 3: Formulate your research question

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

Step 4: Identify variables

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

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

Step 5: State the Null hypothesis

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

Step 6: Select appropriate methods for testing the hypothesis

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

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

Testing and Evaluating Hypotheses

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

• State your research hypothesis.

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

• Collect data strategically.

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

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

• Perform an appropriate statistical test.

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

• Decide if your idea was right or wrong.

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

• Share what you found.

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

The Role of QuestionPro to Develop a Good Research Hypothesis

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

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

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

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

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

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Techathon by QuestionPro: An Amazing Showcase of Tech Brilliance

Jul 3, 2024

Stakeholder Interviews: A Guide to Effective Engagement

Jul 2, 2024

Zero Correlation: Definition, Examples + How to Determine It

Jul 1, 2024

When You Have Something Important to Say, You want to Shout it From the Rooftops

Jun 28, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

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.

Need a helping hand?

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

16 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

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Why are Assumptions Important?

• First Online: 01 January 2012

Cite this chapter

• Apollo M. Nkwake 2  

Assumptions are the foci for any theory and thus any paradigm. It is also important that assumptions are made explicit, and that the number of assumptions is sufficient to describe the phenomenon at hand. Explication of assumptions is even more crucial in research methods used to test the theories. As Mitroff and Bonoma (Evaluation Quarterly 2(2):235–260, 1978) have eloquently put it: “the power of an experiment is only as strong as the clarity of the basic assumptions which underlie it. Such assumptions not only underlie laboratory experimentation but social evaluation research as well.” A scale of articulation of assumptions is represented: (a) Very ambiguously tacit assumptions held in divergent beliefs; (b) Tacit but more obvious assumptions-where parties more or less have shared although unexpressed perceptions or beliefs e.g., legal assumptions in litigation; (c) Informally, explicit assumptions e.g., indirectly expressed or inherent in shared stories, norms, un-codified symbols, and myths; (d) Assumptions that are made explicit. E.g. in theories and models, policies and programs, research and methodology but untested; and (e) Explicit and tested assumptions.

Apparently, assumption is believed to provide an easy or lazy person’s way out of what seems to be, at least at first glance, a perplexing situation. At least, one is not justified in going ahead and making some assumption which moderately expressed is wholly an arbitrary one. Accuracy of interpretation never can be supplanted by assumption. I assume one thing, you assume something else, and the other fellows each assume something different from each of the others. Grant the acceptance of assumption, and then any one of a 100 different solutions is correct. Could anything be more ridiculous? Bennet 1933 , p. 158

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Babbie, E. R. (2007). The practice of social research (11th ed.). Belmont, CA: Thompson Wadsworth.

Google Scholar  

Becker, J., & Niehaves, B. (2007). Epistemological perspectives on IS research: A framework for analyzing and systematizing epistemological assumptions. Information Systems Journal, 17 , 197–214.

Article   Google Scholar  

Bennett, G. E. (1933). Assumptions. The Accounting Review, 8 (2), 157–159.

Bhaskar, R. (1975). A realist theory of science . Leeds: Leeds Books.

Bonell, C. (1999). Evidence based nursing: A stereo-typed view of quantitative and experimental research could work against professional autonomy and authority. Journal of Advanced Nursing, 30 (1), 18–23.

Borg, W., & Gall, M. (1983). Educational research: An introduction (4th ed.). New York: Longman Publishers.

Bredo, E., & Feinberg, W. (1982). Knowledge and values in social and educational research . Philadelphia: Temple University Press.

Bryman, A. (1988). Quantity and quality in social research . London: Routledge.

Book   Google Scholar  

Bryman, A. (1998). Quantitative and qualitative research strategies in knowing the social world. In T. May & M. Williams (Eds.), Knowing the social world . Buckingham and Philadelphia: Open University Press.

Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Qualitative Research, 6 , 97–113.

Campbell, R., & Wasco, S. M. (2000). Feminist approaches to social science: Epistemological and methodological tenets. American Journal of Community Psychology, 28 (6), 773–791.

Carter, S. M., & Little, M. (2007). Justifying knowledge, justifying method, taking action: epistemologies, methodologies, and methods in qualitative research. Qualitative Health Research, 17 (10), 1316–1328.

Clark, A. M. (1998). The qualitative-quantitative debate: moving from positivism and confrontation to post positivism and reconciliation. Journal of Advanced Nursing, 27 , 1242–1249.

Cook, J. A., & Fonow, M. M. (1986). Knowledge and women’s interests: Issues of epistemology and methodology in feminist sociological research. Sociological Inquiry, 56 , 2–29.

Coomer, D. (1984). Critical science: Approach to vocational education research. Journal of Vocational Education Research, 9 (4), 34.

Creswell, J. W., Hanson, W. E., Plano, V. L., & Morales, A. (2007). Qualitative research designs: Selection and implementation. The Counseling Psychologist, 35 (2), 236–264.

Gioia, D. A., & Pitre, E. (1990). Multiparadigm perspectives on theory building. The Academy of Management Review, 15 (4), 584–602.

Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. The Academy of Management Review, 24 (3), 522–537.

Crossan, M., & Guatto, T. (1996). Organizational learning research profile. Journal of Organizational Change Management, 9 (1), 107–112.

Crossan, M., Lane, H., White, R. E., & Djurfeldt, L. (1995). Organizational learning: Dimensions for a theory. International Journal of Organizational Analysis, 3 , 337–360.

Garrick, J. (2010). Doubting the philosophical assumptions of interpretive research. International Journal of Qualitative Studies in Education, 12 (2), 147–156.

Granello, D. H. (2002). Assessing the cognitive development of counseling students: changes in epistemological assumptions. Counselor Education and Supervision, 41 (4), 279–293.

Greene, J. C., & Curucelli, V. (1997). Defining and describing the paradigm issue in mixed-method evaluation. New Directions for Evaluation, 74 (Summer), 5–17.

Greene, J. C., Lipsey, M. W., & Schwandt, T. A. (2007). Method choice: Five discussant commentaries. New Directions for Evaluation, 113 (Spring), 111–118.

Guba, E. G., & Lincoln, Y. S. (1989). Fourth generation evaluation . Newbury Park: Sage.

Hammersley, M. (1995). The politics of social research . London: Sage.

Hawkins, D. F. (1978). Applied research and social theory. Evaluation Quarterly, 2 (1), 141–152.

Healy, M., & Perry, C. (2000). Comprehensive criteria to judge validity and reliability of qualitative research within the realism paradigm. Qualitative Market Research: An International Journal, 3 (3), 118–126.

Hjørland, B. (2002). Epistemology and the socio-cognitive perspective in information science. Journal of the American Society for Information Science and Technology, 53 (4), 257–270.

Hofer, B. K., & Pintrich, P. R. (2002). Personal epistemology: The psychology of beliefs about knowledge and knowing . Mahwah: Erlbaum.

Ingwersen, P. (1996). Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. Journal of Documentation, 52 (1), 3–50.

John, B. E., & Kieras, D. E. (1996). The GOMS family of user interface analysis techniques: Comparison and contrast, ACM transactions on computer. Human Interaction, 3 (4), 320–351.

Kanazawa, S. (1988). In defense of unrealistic assumptions. Sociological Theory, 16 (2), 193–204.

Kaplan, S. A., & Garrett, K. E. (2005). The use of logic models by community-based initiatives. Evaluation and Program Planning, 28 , 167–172.

Kerem, E., Fishman, N., & Josselson, R. (2001). The experience of empathy in everyday relationships: Cognitive and affective elements. Journal of Social and Personal Relationships, 18 (5), 709–729.

Kies, D. (1995). Assumptions. Retrieved July 12, 2011 from http://papyr.com/hypertextbooks/comp2/assume.htm .

Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed.). Chicago: University of Chicago Press.

Lynn, S. J., Weekes, J. R., & Milano, M. J. (1989). Reality versus suggestion: Pseudo memory in hypnotizable and simulating subjects. Journal of Abnormal Psychology, 198 (2), 137–144.

Mason, P., & Barnes, M. (2007). Constructing theories of change: Methods and sources. Evaluation, 13 (2), 151–170.

Mingers, J. A. (2003). Classification of the philosophical assumptions of management science. Journal of the Operational Research Society, 54 (6), 559–570.

Mir, R., & Watson, A. (2001). Critical realism and constructivism in strategy research: Toward a synthesis. Strategic Management Journal, 22 , 1169–1173.

Mitroff, J., & Bonoma, T. V. (1978). Experimentation, and real world problems. A critique and an alternate approach to evaluation of psychological assumptions. Evaluation Quarterly, 2 (2), 235–260.

Morgan, G., & Smircich, L. (1980). The case for qualitative research. The Academy of Management Review, 5 (4), 491–500.

Nagel, E. (1963). Assumptions in economic theory. The American Economic Review, 53 (2), 211–219.

Patton, M. Q. (2011). Developmental evaluation: Applying complexity concepts to enhance innovation and use . New York: Guilford Press.

Perry, C. (1998). Processes of a case study methodology for postgraduate research in marketing. European Journal of Marketing, 32 (9), 785–802.

Robson, C. (1993). Real world research: A resource for social scientists and practitioners-researchers . Oxford: Basil Blackwell.

Sale, J. E., Lohfeld, L. H., & Brazil, K. (2002). Revisiting the quantitative-qualitative debate: Implications for mixed-methods research. Revisiting the Quality and Quantity, 36 , 43–53.

Scates, D. E. (1940). Assumptions underlying research data. The Journal of Educational Research, 34 (4), 241–254.

Scott, D., & Usher, R. S. (1996). Understanding educational research . London: Routledge.

Shermer, M. (2011). The believing brain: From ghosts and gods to politics and conspiracies-How we construct beliefs and reinforce them as truths . New York: Times Books.

Shrivastava, P., & Mitroff, I. I. (1984). Enhancing organizational research utilization: The role of decision makers’ assumptions. The Academy of Management Review, 9 (1), 18–26.

Schwandt, T. A. (2001). Dictionary of qualitative inquiry (2nd ed.). Thousand Oaks: Sage.

Shah, S. K., & Corley, K. G. (2006). Building better theory by bridging the quantitative–qualitative divide. Journal of Management Studies, 43 (8), 1821–1835.

Tsoukas, H. (1991). The missing link: A transformational view of metaphors in organizational science. Academy of Management Review, 16 (1), 566–585.

Ullman-Margalit, E. (1983). On presumption. The Journal of Philosophy, 80 (3), 143–163.

Warner, R. S. (1978). Toward a redefinition of action theory: paying the cognitive element its due. American Journal of Sociology, 83 (6), 1317–1349.

Weiss, C. H. (1995). Nothing as practical as good theory: Exploring theory-based evaluation for comprehensive community initiatives for children and families. In K. Fulbright-Anderson, A. C. Kubrisch, & J. P. Connell (Eds.), New approaches to evaluating community initiatives: Theory, measurement and analysis (Vol. 2, pp. 65–92). Washington, DC: Aspen Institute.

Weiss, C. (2000). Which links in which theories shall we evaluate? New Directions for Evaluation, 87 (Fall), 35–45.

Wegerif, R. (2008). Objective approaches: Dialogic or dialectic? The significance of ontological assumptions in research on educational dialogue. British Educational Research Journal, 34 (3), 347–361.

White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972–1995. Journal of the American Society for Information Science, 49 (4), 327–355.

Williams, D. D. (1986). When is naturalistic evaluation appropriate? New Directions for Program Evaluation, 30 (Summer), 85–92.

Yauch, C. A., & Steudel, H. J. (2003). Complementary use of qualitative and quantitative cultural assessment methods. Organizational Research Methods, 6 (4), 465–481.

Download references

Author information

Authors and affiliations.

World Vision, Inc, I Street NE 300, Washington, DC, 20002, USA

Apollo M. Nkwake

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Apollo M. Nkwake .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Nkwake, A.M. (2013). Why are Assumptions Important?. In: Working with Assumptions in International Development Program Evaluation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4797-9_7

Download citation

DOI : https://doi.org/10.1007/978-1-4614-4797-9_7

Published : 18 August 2012

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-4796-2

Online ISBN : 978-1-4614-4797-9

eBook Packages : Humanities, Social Sciences and Law Social Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don’t worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a grade-school kid with a science-fair project. Concerns with truthful responding, access to participants, and survey instruments are just a few of examples of restrictions on your research. In the following sections, the differences among delimitations, limitations, and assumptions of a dissertation will be clarified.

Delimitations

Delimitations are the definitions you set as the boundaries of your own thesis or dissertation, so delimitations are in your control. Delimitations are set so that your goals do not become impossibly large to complete. Examples of delimitations include objectives, research questions, variables, theoretical objectives that you have adopted, and populations chosen as targets to study. When you are stating your delimitations, clearly inform readers why you chose this course of study. The answer might simply be that you were curious about the topic and/or wanted to improve standards of a professional field by revealing certain findings. In any case, you should clearly list the other options available and the reasons why you did not choose these options immediately after you list your delimitations. You might have avoided these options for reasons of practicality, interest, or relativity to the study at hand. For example, you might have only studied Hispanic mothers because they have the highest rate of obese babies. Delimitations are often strongly related to your theory and research questions. If you were researching whether there are different parenting styles between unmarried Asian, Caucasian, African American, and Hispanic women, then a delimitation of your study would be the inclusion of only participants with those demographics and the exclusion of participants from other demographics such as men, married women, and all other ethnicities of single women (inclusion and exclusion criteria). A further delimitation might be that you only included closed-ended Likert scale responses in the survey, rather than including additional open-ended responses, which might make some people more willing to take and complete your survey. Remember that delimitations are not good or bad. They are simply a detailed description of the scope of interest for your study as it relates to the research design. Don’t forget to describe the philosophical framework you used throughout your study, which also delimits your study.

Limitations

Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results. Do not worry about limitations because limitations affect virtually all research projects, as well as most things in life. Even when you are going to your favorite restaurant, you are limited by the menu choices. If you went to a restaurant that had a menu that you were craving, you might not receive the service, price, or location that makes you enjoy your favorite restaurant. If you studied participants’ responses to a survey, you might be limited in your abilities to gain the exact type or geographic scope of participants you wanted. The people whom you managed to get to take your survey may not truly be a random sample, which is also a limitation. If you used a common test for data findings, your results are limited by the reliability of the test. If your study was limited to a certain amount of time, your results are affected by the operations of society during that time period (e.g., economy, social trends). It is important for you to remember that limitations of a dissertation are often not something that can be solved by the researcher. Also, remember that whatever limits you also limits other researchers, whether they are the largest medical research companies or consumer habits corporations. Certain kinds of limitations are often associated with the analytical approach you take in your research, too. For example, some qualitative methods like heuristics or phenomenology do not lend themselves well to replicability. Also, most of the commonly used quantitative statistical models can only determine correlation, but not causation.

Assumptions

Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis. In other words, any scholar reading your paper will assume that certain aspects of your study is true given your population, statistical test, research design, or other delimitations. For example, if you tell your friend that your favorite restaurant is an Italian place, your friend will assume that you don’t go there for the sushi. It’s assumed that you go there to eat Italian food. Because most assumptions are not discussed in-text, assumptions that are discussed in-text are discussed in the context of the limitations of your study, which is typically in the discussion section. This is important, because both assumptions and limitations affect the inferences you can draw from your study. One of the more common assumptions made in survey research is the assumption of honesty and truthful responses. However, for certain sensitive questions this assumption may be more difficult to accept, in which case it would be described as a limitation of the study. For example, asking people to report their criminal behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that your limitations and assumptions should not contradict one another. For instance, if you state that generalizability is a limitation of your study given that your sample was limited to one city in the United States, then you should not claim generalizability to the United States population as an assumption of your study. Statistical models in quantitative research designs are accompanied with assumptions as well, some more strict than others. These assumptions generally refer to the characteristics of the data, such as distributions, correlational trends, and variable type, just to name a few. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

Click here to cancel reply.

You must be logged in to post a comment.

Copyright © 2024 PhDStudent.com. All rights reserved. Designed by Divergent Web Solutions, LLC .

Assumption vs. Hypothesis

What's the difference.

Assumption and hypothesis are both concepts used in research and reasoning, but they differ in their nature and purpose. An assumption is a belief or statement that is taken for granted or accepted as true without any evidence or proof. It is often used as a starting point or a premise in an argument or analysis. On the other hand, a hypothesis is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated to be tested and verified through empirical research or experimentation. While assumptions are often subjective and can be biased, hypotheses are more objective and aim to provide a basis for scientific investigation.

Further Detail

Introduction.

Assumptions and hypotheses are fundamental concepts in the fields of logic, science, and research. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of assumptions and hypotheses, their roles in different contexts, and how they contribute to the process of knowledge acquisition and problem-solving.

Assumptions

An assumption is a belief or statement that is taken for granted or accepted as true without any proof or evidence. It serves as a starting point for reasoning or argumentation. Assumptions can be based on personal experiences, cultural norms, or generalizations. They are often used to fill in gaps in knowledge or to simplify complex situations.

One key attribute of assumptions is that they are not necessarily true or proven. They are subjective and can vary from person to person. Assumptions can be implicit, meaning they are not explicitly stated, or explicit, where they are clearly expressed. They can also be conscious or unconscious, depending on whether we are aware of them or not.

Assumptions play a crucial role in everyday life, decision-making, and problem-solving. They help us make sense of the world and navigate through uncertain situations. However, it is important to recognize that assumptions can introduce biases and limit our understanding if they are not critically examined or challenged.

A hypothesis, on the other hand, is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated as a testable statement that can be supported or refuted through empirical observation or experimentation. Hypotheses are commonly used in scientific research to guide investigations and generate new knowledge.

Unlike assumptions, hypotheses are grounded in evidence and are subject to verification. They are formulated based on existing theories, observations, or logical reasoning. Hypotheses are often stated in the form of "if-then" statements, where the independent variable (the "if" part) is manipulated or observed to determine its effect on the dependent variable (the "then" part).

Hypotheses are essential in the scientific method, as they provide a framework for conducting experiments and gathering data. They allow researchers to make predictions and draw conclusions based on empirical evidence. If a hypothesis is supported by the data, it can lead to the development of theories or further research. If it is refuted, it may prompt the formulation of new hypotheses or the revision of existing ones.

Comparison of Attributes

While assumptions and hypotheses share the commonality of being statements or beliefs, they differ in several key attributes:

Assumptions are often based on personal beliefs, experiences, or cultural norms. They can be influenced by subjective factors and may not have a solid foundation in evidence or logic. In contrast, hypotheses are grounded in existing knowledge, theories, or observations. They are formulated based on logical reasoning and are subject to empirical testing.

2. Verifiability

Assumptions are not easily verifiable since they are often subjective or based on incomplete information. They are accepted as true without rigorous testing or evidence. On the other hand, hypotheses are formulated to be testable and verifiable. They can be supported or refuted through empirical observation or experimentation.

Assumptions are primarily used to simplify complex situations, fill in gaps in knowledge, or provide a starting point for reasoning. They are often employed in everyday life, decision-making, and problem-solving. Hypotheses, on the other hand, serve the purpose of generating new knowledge, guiding scientific research, and making predictions about the relationship between variables.

4. Role in Knowledge Acquisition

Assumptions can limit knowledge acquisition if they are not critically examined or challenged. They can introduce biases and prevent us from exploring alternative explanations or perspectives. Hypotheses, on the other hand, contribute to knowledge acquisition by providing a structured approach to testing and refining ideas. They encourage critical thinking, data collection, and analysis.

5. Testability

Assumptions are often difficult to test since they are not formulated as specific statements or predictions. They are more subjective in nature and may not lend themselves to empirical verification. Hypotheses, on the other hand, are designed to be testable. They are formulated as specific statements that can be supported or refuted through observation or experimentation.

Assumptions and hypotheses are both important concepts in reasoning, problem-solving, and scientific research. While assumptions provide a starting point for reasoning and decision-making, hypotheses offer a structured approach to generating new knowledge and making predictions. Understanding the attributes and differences between assumptions and hypotheses is crucial for critical thinking, avoiding biases, and advancing our understanding of the world.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Last updated 27/06/24: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > Statistical Hypothesis Testing in Context
• > Assumptions and Hypothesis Tests

Book contents

• Frontmatter
• 1 Introduction
• 2 Theory of Tests, p-Values, and Confidence Intervals
• 3 From Scientific Theory to Statistical Hypothesis Test
• 4 One-Sample Studies with Binary Responses
• 5 One-Sample Studies with Ordinal or Numeric Responses
• 6 Paired Data
• 7 Two-Sample Studies with Binary Responses
• 8 Assumptions and Hypothesis Tests
• 9 Two-Sample Studies with Ordinal or Numeric Responses
• 10 General Methods for Frequentist Inferences
• 11 k-Sample Studies and Trend Tests
• 12 Clustering and Stratification
• 13 Multiplicity in Testing
• 14 Testing from Models
• 15 Causality
• 16 Censoring
• 17 Missing Data
• 18 Group Sequential and Related Adaptive Methods
• 19 Testing Fit, Equivalence, and Noninferiority
• 20 Power and Sample Size
• 21 Bayesian Hypothesis Testing
• Notation Index
• Concept Index

8 - Assumptions and Hypothesis Tests

Published online by Cambridge University Press:  17 April 2022

This short chapter formally defines hypothesis tests in terms of decision rules paired with assumptions. It defines when one set of assumptions is more restrictive than another set. It further defines a multiple perspective decision rule, where one decision rule (and hence its p-value function) can be applied under different sets of assumptions, called perspectives.For example, the one sample t-test p-value may be interpreted as testing a mean restricted to the class of normal distributions, but that same p-value is asymptotically valid under an expanded class of distributions that does not require the normality assumption. This multiple perspective decision rule formulation may allow an appropriate interpretation the t-test p-value even when the data are clearly not normally distributed. Another example is presented, giving the Wilcoxon–Mann–Whitney decision rule under two different perspectives.

Access options

Save book to kindle.

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Assumptions and Hypothesis Tests
• Michael P. Fay , Erica H. Brittain
• Book: Statistical Hypothesis Testing in Context
• Online publication: 17 April 2022
• Chapter DOI: https://doi.org/10.1017/9781108528825.009

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

Public Health Notes

Your partner for better health, hypothesis in research: definition, types and importance .

April 21, 2020 Kusum Wagle Epidemiology 0

Table of Contents

What is Hypothesis?

• Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
• In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.

Different Types of Hypothesis:

1. Simple Hypothesis:

• A Simple hypothesis is also known as composite hypothesis.
• In simple hypothesis all parameters of the distribution are specified.
• It predicts relationship between two variables i.e. the dependent and the independent variable

2. Complex Hypothesis:

• A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.

3. Working or Research Hypothesis:

• A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.

4. Null Hypothesis:

• A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .

5. Alternative Hypothesis:

• An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .

6. Logical Hypothesis:

• A logical hypothesis is a planned explanation holding limited evidence.

7. Statistical Hypothesis:

• A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.

Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:

Major Differences Between Null Hypothesis and Alternative Hypothesis:

Importance of Hypothesis:

• It ensures the entire research methodologies are scientific and valid.
• It helps to assume the probability of research failure and progress.
• It helps to provide link to the underlying theory and specific research question.
• It helps in data analysis and measure the validity and reliability of the research.
• It provides a basis or evidence to prove the validity of the research.
• It helps to describe research study in concrete terms rather than theoretical terms.

Characteristics of Good Hypothesis:

• Should be simple.
• Should be specific.
• Should be stated in advance.

References and For More Information:

https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf

https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance

https://stattrek.com/hypothesis-test/hypothesis-testing.aspx

http://davidmlane.com/hyperstat/A2917.html

https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html

https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html

https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics

• Characteristics of Good Hypothesis
• complex hypothesis
• example of alternative hypothesis
• example of null hypothesis
• how is null hypothesis different to alternative hypothesis
• Importance of Hypothesis
• null hypothesis vs alternate hypothesis
• simple hypothesis
• Types of Hypotheses
• what is alternate hypothesis
• what is alternative hypothesis
• what is hypothesis?
• what is logical hypothesis
• what is null hypothesis
• what is research hypothesis
• what is statistical hypothesis
• why is hypothesis necessary

Copyright © 2024 | WordPress Theme by MH Themes

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

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

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

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

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

Difference Between a Hypothesis and an Assumption

The difference between a hypothesis and an assumption is that the first is typically explicit and the second implicit. A hypothesis is what you are testing explicitly in an experiment. An assumption is tested implicitly. By making your assumptions and hypotheses explicit, you increase the clarity of your approach and the chance for learning.

A: When you are looking for early customers, the value hypothesis is critical. You may reach them using non-scalable methods that don’t address your first real growth hypothesis.

Here is my take on the distinction between a hypothesis and an assumption; your mileage may vary:

A hypothesis is what you are testing explicitly in an experiment. An assumption is tested implicitly. By making your assumptions and hypotheses explicit, you increase the clarity of your approach and the chance for learning.

The two things that can trip you up most often are an unconscious assumption that masks a problem with your hypothesis or an unconscious bias in who you are testing the value hypothesis on. In particular, you may have defined your target customer with specific selection criteria, but your actual choices for whom to speak to (or who will talk with you) are not sampling from the full spectrum of possibilities.

“Creative leaps are discontinuities , qualitative changes. They involve three steps: identification of self-imposed constraints (assumptions); removing them; exploring the consequences of their removal. That is why there is always an element of surprise when we are exposed to creative work–it always embodies the denial of something we have taken for granted, usually unconsciously.” Russell Ackoff in “The Democratic Corporation” (page 99)

Update: Test Your Value Hypothesis First Even If Your Methods Don’t Scale

Update Wed-Jan-29-2014 : Tim Allan left a great comment that elaborated on the need to focus on value first even if your methods don’t scale:

There was a bit of a light-bulb moment for me what I read the line: “When you are looking for early customers the value hypothesis is critical. You may reach them using non-scalable methods that don’t address your first real growth hypothesis.”

I feel this is so often forgotten, especially in the situation of legacy systems and trying to execute lean product design within larger organizations. One example that I have been involved in, and which I regret not pushing back harder, was a requirement to use some legacy data services.

This meant that we couldn’t initially execute a hand-cranked, non-scalable solution to data storage and retrieval that our product required, which would have been better as it would have enabled us to get to customer quicker and get real learnings about how they are using our product.

At the time it didn’t seem like a big deal, but in the end it was, and continues to be an issue and an impediment in getting to the customer quicker. Likewise, any real growth hypothesis, results will most likely be skewed by the performance of systems that are not in your control.

I want to thank Tim for offering a practical story that elaborates on the principle of “confirm the value before worrying about scaling.” When I was at Cisco, the focus was always on “will it scale,” as in we shouldn’t do something because “it won’t scale.” This sometimes led to us releasing a product that could have been more valuable if we had proceeded a little more thoughtfully and incorporated early feedback before rushing to launch. Techniques that work “in the small” to gather insight have their place even inside large firms.

Related Blog Posts

• Hypothesis Not Hype
• Early Markets Offer Fluid Opportunities
• Spend Some Time On How To Disprove Your Hypotheses
• Janet Iwasa: Animate Your Hypothesis to Explore Its Viability and Implications
• Without A Revenue Hypothesis Your Business Model Is a List of User Activities
• Strategy is a Hypothesis
• Tristan Kromer on Testing Customer and Value Hypotheses

Image Credit: Public Domain image of a “Rubin Vase”

7 thoughts on “Difference Between a Hypothesis and an Assumption”

How refreshing to find your insights! Behind every strategy or project design is a set of hypotheses, usually not made explicit. We can use if-then causal thinking to link objectives together in relationship to each other with if-then logic “if we build it, they will come”.

My life work has been using the Logical Framework Approach, a systems thinking and project design methodology based on hypotheses and assumptions. Featured in book “Strategic Project Management Made Simple”, and video http://youtu.be/IX09_y4O1aI

Thanks Sean for bringing strategic management principles to your readers — what you shared is fundamental to success but seldom explored.

There was a bit of a light-bulb moment for me what I read the line:

“When you are looking for early customers the value hypothesis is critical. You may reach them using non-scalable methods that don’t address your first real growth hypothesis.”

I feel this is so often forgotten, especially in the situation of legacy systems and trying to execute lean product design within larger organisations. One example that I have been involved in, and which I regret not pushing back harder, was a requirement to use some legacy data services.

This meant that we couldn’t initially execute a hand-cranked, non-scalable solution to data storage and retrieval that our product required, which would have been better as it would have enabled us to get to customer quicker and get real learnings about how they are using our product.

At the time it didn’t seem like a big deal, but in the end it was, and continues to be an issue and an impediment in getting to the customer quicker. Likewise, any real growth hypothesis, results will most likely be skewed by the performance of systems that are not in your control.

Pingback: SKMurphy, Inc. » Quotes for Entrepreneurs–January 2014

Pingback: SKMurphy, Inc. Quotes for Entrepreneurs-November 2014 - SKMurphy, Inc.

Pingback: SKMurphy, Inc. Customer Development After The First Prototype

Pingback: SKMurphy, Inc. Strategy is a Hypothesis - SKMurphy, Inc.

Pingback: SKMurphy, Inc. Most Popular SKMurphy Blog Posts Oct-2006 Through Sep-2017 - SKMurphy, Inc.

Leave a Comment

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.