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• 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 .

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to 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

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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.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved April 5, 2024, from https://www.scribbr.com/methodology/hypothesis/

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Research Questions & Hypotheses

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

Research Questions

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

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

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

Research Hypotheses

Present the researcher’s predictions based on specific statements.

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

Types of Research Hypothesis

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

Tips for developing research questions and hypotheses for research studies

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

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

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

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

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

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 2 April 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

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

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

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

To form a hypothesis, you should take these steps:

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

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

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

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

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

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

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

A Word From Verywell

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

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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

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

Quantitative Research in Mass Communications : R and RStudio

7 formulating research questions and hypotheses, 7.1 introduction to research questions and hypotheses.

In the realm of academic research, particularly within the field of mass communications, the formulation of research questions and hypotheses is a foundational step that sets the direction and scope of a study. These elements are crucial not only for guiding the research process but also for defining the study’s objectives and expectations. This section highlights the significance of research questions and hypotheses and elucidates the role they play in framing a study.

The Importance of Research Questions and Hypotheses in Guiding Research

Defining the Research Focus: Research questions serve as the cornerstone of any study, clearly outlining the specific issue or phenomenon that the research aims to explore. They help narrow down the broad area of interest into a focused inquiry that can be systematically investigated.

Guiding Methodology: The nature of the research question—whether it seeks to describe, compare, or determine cause and effect—directly influences the choice of research design, methods, and analysis techniques. Well-formulated questions ensure that the research methodology is appropriately aligned with the study’s objectives.

Facilitating Hypothesis Formulation: In quantitative research, hypotheses often stem from the research questions, proposing specific predictions or expectations based on theoretical foundations or previous studies. Hypotheses provide a testable statement that guides the empirical investigation and analysis.

7.1.1 Overview of the Role These Elements Play in Framing a Study

Structuring the Research Framework: Together, research questions and hypotheses establish the conceptual framework for a study, defining its boundaries and specifying the variables of interest. This framework serves as a blueprint, guiding all subsequent steps of the research process.

Informing Literature Review: Research questions and hypotheses inform the scope and focus of the literature review, directing attention to relevant theories, concepts, and empirical findings. This ensures that the review is tightly integrated with the study’s aims and contributes to building a solid theoretical foundation.

Determining Data Collection and Analysis: The formulation of research questions and hypotheses has direct implications for data collection methods, sampling strategies, and analytical techniques. They dictate what data are needed, how they should be collected, and the statistical tests or analytical approaches required to address the research questions and test the hypotheses.

Communicating the Study’s Purpose: Research questions and hypotheses effectively communicate the purpose and direction of the study to the academic community, stakeholders, and the broader public. They articulate the study’s contribution to knowledge, its relevance to theoretical debates or practical issues, and the potential implications of the findings.

In summary, research questions and hypotheses are indispensable components of the research process, serving as the guiding light for the entire study. They provide clarity, direction, and purpose, ensuring that the research is coherent, focused, and methodologically sound. By meticulously crafting these elements, researchers in mass communications lay the groundwork for meaningful and impactful studies that advance our understanding of complex media landscapes and communication dynamics.

7.2 Understanding Research Questions

Research questions are the foundation of any scholarly inquiry, guiding the direction and focus of the study. In mass communications research, where topics can range from analyzing media effects to understanding audience behaviors, formulating effective research questions is crucial for defining the scope and objectives of a study. This section delves into the definition and characteristics of a good research question, distinguishes between exploratory and descriptive research questions, and discusses strategies for developing clear and focused questions.

Definition and Characteristics of a Good Research Question

Definition: A research question is a clearly formulated question that outlines the issue or problem your study aims to address. It sets the stage for the research design, data collection, and analysis, directing the inquiry toward a specific goal.

Characteristics of a Good Research Question:

• Clarity: It should be clearly stated, avoiding ambiguity and ensuring that the research focus is understandable to others.
• Relevance: The question should be significant to the field of study, addressing gaps in the literature or emerging issues in mass communications.
• Researchability: It must be possible to answer the question through empirical investigation, using available research methods and tools.
• Specificity: A good question is specific, targeting a particular aspect of the broader topic to make the research manageable and focused.

Distinction Between Exploratory and Descriptive Research Questions

Exploratory Research Questions: These questions are used when little is known about the topic or phenomenon. Exploratory questions aim to investigate and gain insights into a subject, seeking to understand how or why something happens. In mass communications, an exploratory question might ask, “How do emerging social media platforms influence political engagement among young adults?”

Descriptive Research Questions: Descriptive questions aim to describe the characteristics or features of a subject. They are used when the goal is to provide an accurate representation or count of a phenomenon. A descriptive research question in mass communications might be, “What are the predominant themes in news coverage of environmental issues?”

Developing Clear and Focused Research Questions

• Specificity: Your research question should be narrowly tailored to address a specific issue within the broader field of mass communications. This specificity helps in defining the study’s scope and focusing the research efforts.
• Feasibility: Consider the practical aspects of answering your research question, including the availability of data, time constraints, and resource limitations. A feasible question is one that can be realistically investigated within the parameters of your study.
• Literature Review: Conduct a thorough review of existing research to identify gaps or unresolved questions in the field. This can inspire focused and relevant research questions.
• Consultation: Discuss your ideas with peers, mentors, or experts in mass communications. Feedback can help refine your questions and ensure they are both specific and feasible.
• Pilot Studies: Small-scale pilot studies or preliminary investigations can provide insights that help in formulating or refining your research questions.

Crafting clear and focused research questions is a critical step in the research process, setting the stage for meaningful and impactful inquiry. By ensuring that your questions are specific, feasible, and relevant to the field of mass communications, you lay the groundwork for a study that can contribute valuable insights to our understanding of media and communication phenomena.

7.3 Types of Research Questions

In the pursuit of scientific inquiry within mass communications, research questions serve as the navigational compass guiding the research process. These questions can be broadly categorized into two types: nondirectional and directional. Each type serves a distinct purpose and is formulated based on the nature of the study and the specific objectives the researcher aims to achieve. This section explores the definitions, uses, and strategies for crafting both nondirectional and directional research questions.

Nondirectional Research Questions

Definition: Nondirectional research questions are open-ended queries that explore the existence of a relationship between variables without specifying the anticipated direction of this relationship. They are used when the literature does not strongly suggest which outcome is expected or when exploring new or under-researched areas.

When to Use Them: Employ nondirectional questions when previous research is inconclusive, conflicting, or absent. They are particularly useful in exploratory studies where the aim is to uncover patterns, relationships, or phenomena without presupposing outcomes.

Crafting Questions:

• Focus on Exploration: Phrase your question to emphasize exploration, such as “Is there a relationship between social media usage and political participation among young adults?”
• Avoid Implied Direction: Ensure the wording does not inadvertently suggest a presumed direction of the relationship. The question should remain open to any outcome, whether positive, negative, or neutral.

Directional Research Questions

Definition: Directional research questions specify the expected direction of the relationship between variables. These questions are based on predictions that are often derived from theoretical frameworks or existing literature.

Purposes: Directional questions are used when there is sufficient theoretical or empirical basis to hypothesize a particular outcome. They guide the research towards testing specific hypotheses, making them suitable for studies aiming to confirm or refute theoretical predictions.

Formulating Questions:

• Specify Expected Outcomes: Clearly articulate the anticipated direction of the relationship in the question. For example, “Does increased exposure to environmental news lead to higher levels of environmental activism among viewers?”
• Ground in Literature: Ensure that the directionality implied by your question is supported by theoretical rationales or empirical evidence from previous research. This alignment strengthens the justification for expecting a particular outcome.

7.4 Strategies for Formulating Research Questions

Regardless of the type, crafting effective research questions requires a deep understanding of the topic at hand, a thorough review of the existing literature, and a clear articulation of the research’s goals. Here are some strategies to consider:

• Engage with Current Research: Immerse yourself in the latest studies and debates within the field of mass communications to identify trends, gaps, and areas ripe for investigation.
• Consult Theoretical Frameworks: Draw on established theories to guide the formulation of your questions, whether seeking to explore uncharted territory (nondirectional) or test specific propositions (directional).
• Iterative Refinement: Research questions often evolve during the initial stages of a study. Be prepared to refine your questions as you delve deeper into the literature and sharpen your study’s focus.

By thoughtfully selecting the type of research question that best suits the aims and scope of your study, you lay a solid foundation for a coherent, rigorous, and insightful exploration of mass communications phenomena.

7.5 Operationalization of Concepts

Operationalization is a critical process in the research design phase, particularly in quantitative studies within the realm of mass communications. It involves defining the abstract concepts or variables in measurable terms, determining how they will be observed, measured, or manipulated within the study. This section outlines the essence of operationalization, its pivotal role in research, the steps involved in operationalizing variables, and provides examples pertinent to mass communications research.

Defining Operationalization and Its Significance in Research

Definition: Operationalization is the process by which researchers define how to measure or manipulate the variables of interest in a study. It transforms theoretical constructs into measurable indicators, allowing for empirical observation and quantitative analysis.

Significance: The operationalization of concepts is fundamental to ensuring the reliability and validity of a study. By clearly specifying how variables are measured, researchers enable the replication of the study, enhance the clarity and coherence of their research design, and facilitate the objective analysis of findings.

Steps to Operationalize Variables

Identify the Key Concepts: Begin by clearly identifying the key concepts or variables you intend to study. In mass communications, this might include phenomena like media influence, audience engagement, or digital literacy.

Define the Variables Conceptually: Provide clear, conceptual definitions for each variable, drawing on existing literature or theoretical frameworks to delineate the boundaries of the concept.

Specify the Variables Operationally: Decide on the specific operations, techniques, or instruments you will use to measure or manipulate each variable. This includes determining the type of data to be collected, the scale of measurement, and the method of data collection.

Develop or Select Measurement Instruments: Choose or develop instruments that accurately measure your operationalized variables. This could involve creating surveys, designing experiments, or developing coding schemes for content analysis.

Pilot Test: Conduct a pilot test of your measurement instruments to ensure they effectively capture the operationalized variables. Adjustments based on feedback from the pilot test can improve the reliability and validity of the measures.

Examples of Operationalizing Common Variables in Mass Communications Research

Audience Engagement: Conceptually defined as the level of interaction and involvement an individual has with media content. Operationally, it could be measured through the number of social media shares, comments, or time spent viewing content.

Media Influence on Public Opinion: Conceptually, this refers to the impact media content has on shaping individuals’ attitudes and beliefs. Operationally, it could be measured by changes in attitudes before and after exposure to specific media messages, using pretest-posttest surveys.

Digital Literacy: Conceptually defined as the ability to find, evaluate, create, and communicate information using digital technologies. Operationally, digital literacy could be measured through a questionnaire assessing skills in these areas, with items rated on a Likert scale.

Operationalization is a cornerstone of rigorous research methodology, bridging the gap between theoretical concepts and empirical evidence. By meticulously defining and measuring variables, researchers in mass communications can ground their studies in observable reality, enhancing the validity of their findings and contributing meaningful insights into the complex dynamics of media and communication.

7.6 Developing Hypotheses

In the framework of quantitative research, particularly within the expansive field of mass communications, hypotheses serve as pivotal elements that further refine and operationalize the research questions. This section elucidates the definition and function of hypotheses in quantitative research, explores the relationship between research questions and hypotheses, and outlines the criteria that make a hypothesis testable.

Definition and Function of Hypotheses in Quantitative Research

Definition: A hypothesis is a predictive statement that proposes a possible outcome or relationship between two or more variables. It is grounded in theory or prior empirical findings and serves as a basis for scientific inquiry.

Function: The primary function of a hypothesis is to provide a specific, testable proposition derived from the broader research question. Hypotheses guide the research design, data collection, and analysis process, offering a clear focus for empirical investigation. They enable researchers to apply statistical methods to test the proposed relationships or effects, thereby contributing to the accumulation of scientific knowledge.

The Relationship Between Research Questions and Hypotheses

From Questions to Hypotheses: Research questions set the stage for the research by identifying the key phenomena or relationships of interest. Hypotheses take this a step further by specifying the expected direction or nature of these relationships based on theoretical or empirical groundwork. Essentially, while research questions identify “what” the study aims to explore, hypotheses propose “how” these explorations will unfold.

Complementarity: Research questions and hypotheses are complementary, with the former providing a broad inquiry framework and the latter offering a focused, conjectural answer that can be empirically tested. This synergy ensures that the research is both guided by curiosity and anchored in a framework that facilitates systematic investigation.

Criteria for a Testable Hypothesis

For a hypothesis to effectively contribute to the research process, it must be testable. The following criteria are essential for constructing a hypothesis that can be empirically evaluated:

Specificity: A testable hypothesis must clearly and specifically define the variables involved and the expected relationship between them. This clarity ensures that the hypothesis can be directly linked to observable and measurable outcomes.

Empirical Referents: The variables within the hypothesis must have empirical referents – that is, they must be capable of being measured or manipulated in the real world. This allows the hypothesis to be subjected to empirical testing.

Predictive Nature: A testable hypothesis should make a predictive statement about the expected outcome of the study, enabling the research to confirm or refute the proposed relationship or effect based on empirical evidence.

Grounding in Theory or Prior Research: The hypothesis should be grounded in existing theoretical frameworks or empirical findings, providing a rationale for the expected relationship or outcome. This grounding not only lends credibility to the hypothesis but also ensures that it contributes to the ongoing academic discourse.

Falsifiability: Finally, a testable hypothesis must be falsifiable. This means it should be possible to conceive of an outcome that would contradict the hypothesis, allowing for the possibility of it being disproven through empirical evidence.

Developing well-crafted hypotheses is a critical step in the quantitative research process, particularly in mass communications, where the rapid evolution of media technologies and platforms continually opens new avenues for inquiry. By adhering to these criteria, researchers can ensure that their hypotheses are not only testable but also meaningful, contributing valuable insights to our understanding of complex media landscapes and their impacts on society.

7.7 Types of Hypotheses

In the empirical research landscape, especially within the domain of mass communications, hypotheses are indispensable tools that guide the investigative process. They are typically categorized into null hypotheses and alternative hypotheses, each serving a distinct role in framing the research inquiry. This section provides definitions for these two types of hypotheses, discusses their roles in research, and offers guidance on formulating them effectively.

Null Hypotheses (H0)

Definition: The null hypothesis (H0) posits that there is no difference, effect, or relationship between the variables under investigation. It represents a statement of skepticism or neutrality, suggesting that any observed differences or relationships in the data are due to chance rather than a systematic effect.

Role in Research: The null hypothesis serves as a benchmark for testing the existence of an effect or relationship. By attempting to disprove or reject the null hypothesis through statistical analysis, researchers can provide evidence supporting the presence of a meaningful effect or relationship. The null hypothesis is foundational in hypothesis testing, enabling researchers to apply statistical methods to determine the likelihood that observed data could have occurred under the null condition.

Formulating Null Hypotheses: Null hypotheses are formulated as statements of no difference or no relationship. For example, in a study examining the impact of social media usage on political engagement, a null hypothesis might state, “There is no difference in political engagement levels between users and non-users of social media.”

Alternative Hypotheses (H1)

Definition: The alternative hypothesis (H1) is the counter proposition to the null hypothesis. It posits that there is a significant difference, effect, or relationship between the variables being studied. The alternative hypothesis reflects the researcher’s theoretical expectation or prediction about the outcome of the study.

Complementing Null Hypotheses: The alternative hypothesis directly complements the null hypothesis by specifying the expected effect or relationship that the research aims to demonstrate. While the null hypothesis posits the absence of an effect, the alternative hypothesis asserts its presence, guiding the direction of the study’s empirical investigation.

Crafting Alternative Hypotheses: Alternative hypotheses are crafted to predict specific outcomes based on the research question and theoretical framework. They should clearly articulate the anticipated direction or nature of the relationship or difference between variables. Continuing the earlier example, an alternative hypothesis might state, “Users of social media exhibit higher levels of political engagement than non-users.”

7.8 Strategic Formulation of Hypotheses

The formulation of null and alternative hypotheses is a strategic exercise that sets the stage for empirical testing. Effective hypotheses are:

• Specific and Concise: Clearly define the variables and the expected relationship or difference, avoiding ambiguity.
• Empirically Testable: Ensure that the hypotheses can be tested using available research methods and data.
• Theoretically Grounded: Base your hypotheses on existing literature, theories, or preliminary evidence, providing a rationale for the expected outcomes.

In mass communications research, where the interplay of media, technology, and society offers a rich tapestry of phenomena to explore, the thoughtful formulation of null and alternative hypotheses is crucial. It not only delineates the scope of the investigation but also ensures that the research contributes meaningful insights into the dynamics of communication processes and their impacts.

7.9 Directional and Nondirectional Hypotheses

In the nuanced world of quantitative research, particularly within the field of mass communications, hypotheses serve as a bridge between theoretical inquiry and empirical investigation. They are typically formulated as either directional or nondirectional, each with specific implications for the study’s design and analysis. This section clarifies the distinction between these two types of hypotheses and provides guidance on when to use each, complemented by examples from mass communications research.

Understanding the Distinction and When to Use Each Type

Directional Hypotheses: Directional hypotheses specify the expected direction of the relationship or difference between variables. They are based on theoretical predictions or empirical evidence suggesting a particular outcome. Directional hypotheses are used when prior research or theory provides a strong basis for anticipating the direction of the effect.

Nondirectional Hypotheses: Nondirectional hypotheses indicate that a relationship or difference exists between variables but do not specify the direction. They are appropriate when there is uncertainty about the expected outcome or when previous studies have yielded mixed or inconclusive results.

Examples of Both Directional and Nondirectional Hypotheses in Mass Communications Research

• “Individuals who frequently engage with news content on social media platforms will exhibit higher levels of political awareness than those who do not engage with news content on these platforms.” This hypothesis predicts a specific direction of the relationship between social media news engagement and political awareness.
• “Exposure to environmental documentaries will increase viewers’ concern for environmental issues more than exposure to traditional news coverage of the same issues.” This hypothesis specifies an expected difference in the effect of two types of media content on environmental concern.
• “There is a relationship between the frequency of smartphone use for social media and the level of social isolation experienced by young adults.” This hypothesis suggests a relationship exists but does not predict whether more frequent use increases or decreases social isolation.
• “The introduction of interactive digital learning tools in communication courses affects students’ academic performance.” This hypothesis indicates that an effect is expected but does not specify whether the effect is positive or negative on academic performance.

7.10 Deciding Between Directional and Nondirectional Hypotheses

The choice between directional and nondirectional hypotheses hinges on several factors:

• Theoretical Basis: Strong theoretical foundations or extensive empirical evidence supporting a specific outcome favor the use of directional hypotheses.
• Research Objectives: Exploratory studies aiming to identify patterns or relationships might initially employ nondirectional hypotheses, especially in emerging areas of mass communications where less is known.
• Statistical Considerations: Directional hypotheses allow for more focused statistical tests (e.g., one-tailed tests), which can be more powerful in detecting specified effects. However, they require a strong justification for predicting the direction of the effect.

By carefully considering these factors, researchers in mass communications can effectively choose the type of hypothesis that best suits their study’s objectives and theoretical framework. Whether directional or nondirectional, the formulation of hypotheses is a critical step in the research process, guiding empirical inquiry and contributing to the advancement of knowledge in the dynamic field of mass communications.

7.11 Criteria for Good Research Questions and Hypotheses

In the rigorous academic landscape of mass communications research, the construction of research questions and hypotheses serves as the bedrock upon which studies are built and conducted. These foundational elements not only guide the direction of the research but also determine its scope, focus, and potential contribution to the field. To ensure the effectiveness and integrity of research, certain criteria must be met. This section outlines the essential qualities of good research questions and hypotheses: clarity and precision, relevance to the field of study, and researchability with empirical testing potential.

Clarity and Precision

Definition: Clarity in research questions and hypotheses means that they are stated in a straightforward and unambiguous manner, easily understood by those within and outside the field. Precision involves the specific delineation of the variables and constructs involved, leaving no room for misinterpretation.

Importance: Clear and precise formulations allow for a focused investigation, guiding the research design, data collection, and analysis process. They ensure that the study addresses the intended concepts and relationships directly and effectively.

Strategies for Achieving Clarity and Precision:

• Use specific, defined terms and avoid jargon that may not be universally understood.
• Clearly specify the variables or phenomena being studied and their expected relationships.
• Ensure that hypotheses are directly testable, with defined criteria for confirmation or refutation.

Relevance to the Field of Study

Definition: Relevance implies that the research questions and hypotheses address significant issues, gaps, or debates within the field of mass communications. They should contribute to advancing understanding, theory, or practice in meaningful ways.

Importance: Research that is relevant to the field is more likely to receive attention from scholars, policymakers, and practitioners, and to secure funding and publication opportunities. It ensures that the study contributes to the ongoing discourse and development of mass communications as a discipline.

Strategies for Ensuring Relevance:

• Conduct a thorough review of current literature to identify gaps, emerging trends, or unresolved questions.
• Align research questions and hypotheses with theoretical frameworks or pressing societal issues.
• Consider the practical implications and potential impact of the research on the field.

Researchability and Empirical Testing Potential

Definition: Researchability refers to the feasibility of addressing the research questions and testing the hypotheses through empirical methods. This includes the availability of data, appropriateness of methodology, and the potential for gathering evidence to support or refute the hypotheses.

Importance: For research to contribute to the body of knowledge, it must be capable of being rigorously investigated using empirical methods. Research questions and hypotheses with high empirical testing potential allow for the derivation of meaningful, verifiable insights.

Strategies for Enhancing Researchability:

• Ensure that the variables involved can be accurately measured or observed using existing tools or methods.
• Design hypotheses that are testable within the constraints of time, resources, and ethical considerations.
• Consider the practical aspects of data collection, including access to participants, media content, or archival resources.

Crafting research questions and hypotheses that are clear and precise, relevant to the field, and amenable to empirical investigation is crucial for conducting impactful research in mass communications. These criteria not only guide the research process but also enhance the study’s validity, reliability, and contribution to the field, fostering a deeper understanding of the complex dynamics that shape media and communication in society.

7.12 Common Mistakes to Avoid in Formulating Research Questions and Hypotheses

When embarking on a research project, especially in a field as dynamic as mass communications, the formulation of research questions and hypotheses is a critical step that sets the stage for the entire study. However, researchers, particularly those new to the field, may encounter pitfalls that can compromise the clarity, relevance, and feasibility of their research. This section highlights common mistakes to avoid in the formulation process, ensuring that research questions and hypotheses are both robust and actionable.

Formulating Questions and Hypotheses That Are Too Broad or Vague

Issue: Broad or vague questions and hypotheses lack specificity and focus, making it difficult to define the scope of the study or determine the appropriate methodology for investigation.

Impact: They can lead to an unwieldy research project with diffuse objectives, posing challenges in data collection, analysis, and interpretation of findings.

Avoidance Strategy: Narrow down the research topic by focusing on specific aspects, populations, or contexts. Use the literature review to identify gaps and refine the research focus to a manageable scope.

Confusing Research Questions with Interview or Survey Questions

Issue: There is a distinction between overarching research questions that guide a study and the specific questions posed in interviews or surveys. Confusing the two can lead to a misalignment between the study’s objectives and the data collection process.

Impact: This confusion can result in collecting data that do not effectively address the research questions, undermining the study’s ability to generate meaningful insights.

Avoidance Strategy: Clearly delineate between the broad research questions that frame your study and the specific items or prompts used in data collection instruments. Ensure that each interview or survey question is directly linked to and serves the purpose of answering the overarching research questions.

Creating Untestable Hypotheses

Issue: Hypotheses that are not empirically testable, either due to the abstract nature of the constructs involved or the lack of available methods for measurement, pose significant challenges to the research process.

Impact: Untestable hypotheses cannot be substantiated or refuted through empirical evidence, limiting the study’s contribution to the field and its scientific merit.

Avoidance Strategy: Ensure that all variables in the hypothesis can be measured or manipulated with existing research methods. Operationalize abstract concepts clearly and consider the feasibility of empirical testing during the hypothesis formulation stage.

7.13 Best Practices for Robust Formulation

Alignment with Theoretical Frameworks: Ground your research questions and hypotheses within established theories or models in mass communications, ensuring they contribute to the broader academic dialogue.

Consultation with Peers and Mentors: Engage in discussions with peers, mentors, or experts in the field to refine your research questions and hypotheses, leveraging their insights to avoid common pitfalls.

Pilot Testing: Consider conducting a pilot study or preliminary analysis to test the feasibility of your research questions and hypotheses, allowing for adjustments before the full-scale study.

By avoiding these common mistakes and adhering to best practices, researchers can formulate research questions and hypotheses that are clear, focused, and empirically testable. This careful preparation enhances the quality and impact of research in mass communications, contributing valuable insights into the complex interplay between media, technology, and society.

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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Formulating the Research Question and Framing the Hypothesis

Affiliation.

• 1 Respiratory Care Services, Arkansas Children's Hospital, Little Rock, Arkansas. [email protected].
• PMID: 37041024
• PMCID: PMC10353175 (available on 2024-08-01 )
• DOI: 10.4187/respcare.10975

An understanding of the research process is an essential skill for designing a study and developing the research protocol. Poor study design can lead to fatal flaws in research methodology, ultimately resulting in rejection for publication or limiting the reliability of the results. Following the steps of the research process and devising the research question and hypothesis prior to study initiation can avoid common problems encountered with research questions and study design. Formulating the research question is the first step in the research process and provides the foundation for framing the hypothesis. Research questions should be feasible, interesting, novel, ethical, and relevant (FINER). Application of the FINER criteria can assist with ensuring the question is valid and will generate new knowledge that has clinical impact. Utilization of the population, intervention, comparison, and outcome (PICO) format helps to structure the question as well as refine and narrow the focus from a broad topic. The hypothesis is derived from the research question and is used to determine the experiments or interventions that will answer the question. This aim of this paper is to provide guidance for developing research questions and forming a testable hypothesis through application of the FINER criteria and the PICO process.

Keywords: FINER criteria; PICO; hypothesis; research; research question; scientific method; study design.

Copyright © 2023 by Daedalus Enterprises.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

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

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

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

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

How To Conduct Hypothesis Testing In R For Effective Data Analysis

Learn the essentials of hypothesis testing in R, a crucial skill for developers. This article guides you through setting up your environment, formulating hypotheses, executing tests, and interpreting results with practical examples

💡 KEY INSIGHTS

• Hypothesis testing involves using a random population sample to test the null and alternative hypotheses , where the null hypothesis typically represents equality between population parameters​​.
• The null hypothesis (H0) assumes no event occurrence and is critical unless rejected, while the alternate hypothesis (H1) is its logical opposite and is considered upon the rejection of H0​​.
• The p-value is a crucial metric in hypothesis testing, indicating the likelihood of an observed difference occurring by chance; a lower p-value suggests a higher probability of the alternate hypothesis being true​​.
• Hypothesis testing is significant in research methodology as it provides evidence-based conclusions , supports decision-making , adds rigor and validity , and contributes to the advancement of knowledge in various fields​​.

Hypothesis testing in R is a fundamental skill for programmers and developers looking to analyze and interpret data effectively. This article guides you through the essential steps and techniques, using R's robust statistical tools. Whether you're new to R or seeking to refine your data analysis skills, these insights will enhance your ability to make data-driven decisions.

Setting Up Your R Environment

Formulating and testing your hypothesis, interpreting test results, frequently asked questions.

Before diving into hypothesis testing, ensure you have R and RStudio installed. R is the programming language used for statistical computing, while RStudio provides an integrated development environment (IDE) to work with R. Download R from CRAN and RStudio from RStudio's website.

Configuring Your Workspace

Installing necessary packages, loading data into r, exploratory data analysis, basic data manipulation.

After installation, open RStudio and set up your workspace. This involves organizing your scripts, data files, and outputs. Use setwd() to define your working directory:

R's functionality is extended through packages. For hypothesis testing, packages like ggplot2 for data visualization and stats for statistical functions are essential. Install packages using install.packages() :

After installation, load them into your session using library() :

Data can be loaded into R using various functions depending on the file format. For a CSV file, use read.csv() :

Before hypothesis testing, it's crucial to understand your data. Use summary functions and visualization to explore:

Data often requires cleaning and manipulation. Functions like subset() and transform() are useful:

These commands help in refining your dataset, making it ready for hypothesis testing.

The first step in hypothesis testing is to Formulate a Clear Hypothesis . This typically involves stating a null hypothesis (H0) that indicates no effect or no difference, and an alternative hypothesis (H1) that suggests the presence of an effect or a difference.

Null And Alternative Hypothesis

Choosing the right test, t-test example, interpreting the results, analyzing the output, visualizing the data.

For example, if you're testing whether a new programming tool improves efficiency:

• H0: The tool does not improve efficiency.
• H1: The tool improves efficiency.

Selecting an appropriate statistical test is crucial. The choice depends on your data type and the nature of your hypothesis. Common tests include t-tests, chi-square tests, and ANOVA.

If you're comparing means between two groups, a t-test is appropriate. In R, use t.test() :

The output of t.test() includes the P-Value , which helps determine the significance of your results. A p-value lower than your significance level (commonly 0.05) indicates that you can reject the null hypothesis.

After running t.test() , analyze the output:

• P-Value : Indicates the probability of observing your data if the null hypothesis is true.
• Confidence Interval : Provides a range in which the true mean difference likely lies.

Visualizing your data can provide additional insights. For instance, use ggplot2 to create a plot that compares the groups:

Understanding P-Values

Interpreting confidence intervals, effect size, calculating and interpreting effect size, creating a plot for results.

The P-Value is central in interpreting hypothesis test results. It represents the probability of observing your data, or something more extreme, if the null hypothesis is true. A small p-value (typically ≤ 0.05) suggests that the observed data is unlikely under the null hypothesis, leading to its rejection.

Evaluating Significance

When you run a test, R provides a p-value:

Confidence Intervals

Confidence Intervals offer a range of values within which the true parameter value lies with a certain level of confidence (usually 95%). Narrow intervals indicate more precise estimates.

From your test output, extract and examine the confidence interval:

While p-values indicate whether an effect exists, the Effect Size measures its magnitude. It's crucial for understanding the practical significance of your results.

For a t-test, you might calculate Cohen's d:

For instance, create a plot to visualize the difference:

What is Effect Size and Why is it Important?

Effect size is a quantitative measure of the magnitude of the experimental effect. Unlike p-values, which tell you if an effect exists, effect size tells you how large that effect is. It's important for understanding the practical significance of your results.

How Do I Interpret a Confidence Interval?

A confidence interval gives a range of values within which the true value is likely to lie. For example, a 95% confidence interval means that if the same study were repeated many times, 95% of the intervals would contain the true value.

What Does 'Rejecting the Null Hypothesis' Mean in Practical Terms?

Rejecting the null hypothesis suggests that there is enough statistical evidence to support the alternative hypothesis. In practical terms, it means that the observed effect or difference is unlikely to be due to chance.

Can I Perform Hypothesis Testing on Non-Numeric Data?

Yes, you can perform hypothesis testing on non-numeric (categorical) data. Tests like the Chi-Square test are designed for categorical data and can test hypotheses about proportions or frequencies.

Let’s test your knowledge!

What is the function used in R to perform a t-test?

Continue learning with these 'programming' guides.

• How To Debug In R: Effective Strategies For Developers
• How To Use R For Simulation: Effective Strategies And Techniques
• How To Install R Packages: Steps For Efficient Integration
• How To Import Data In R: Essential Steps For Efficient Data Analysis
• How To Clean Data In R: Essential Techniques For Effective Data Management

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