assumption and hypothesis in research sample

Ohio State nav bar

The Ohio State University

• BuckeyeLink
• Find People
• Search Ohio State

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.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• 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 .

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

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

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 13 May 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

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.

Scientific Research Methods

28.2 about hypotheses and assumptions.

Two hypotheses are made about the population parameter:

• The null hypothesis \(H_0\) ; and
• The alternative hypothesis \(H_1\) .

28.2.1 Null hypotheses

Hypotheses always concern a population parameter . Hypothesising, for example, that the sample mean body temperature is equal to \(37.0^\circ\text{C}\) is pointless, because it clearly isn’t: the sample mean is \(36.8051^\circ\text{C}\) . Besides, the RQ is about the unknown population : the P in P OCI stands for P opulation.

The null hypothesis \(H_0\) offers one possible reason why the value of the sample statistic (such as the sample mean) is not the same as the value of the proposed population parameter (such as the population mean): sampling variation . Every sample is different, and so the sample statistic will vary from sample to sample; it may not be equal to the population parameter , just because of the sample used by chance. Null hypotheses always have an ‘equals’ in them (for example, the population mean equals 100, is less than or equal to 100, or is more than or equal to 100), because (as part of the decision making process ), something specific must be assumed for the population parameter.

The parameter can take many different forms, depending on the context. The null hypothesis about the parameter is the default value of that parameter; for example,

• there is no difference between the parameter value in two (or more) groups;
• there is no change in the parameter value; or
• there is no relationship as measured by a parameter value.

28.2.2 Alternative hypotheses

The other hypothesis is called the alternative hypothesis \(H_1\) . The alternative hypothesis offers another possible reason why the value of the sample statistic (such as the sample mean) is not the same as the value of the proposed population parameter (such as the population mean). The alternative hypothesis proposes that the value of the population parameter really is not the value claimed in the null hypothesis.

Alternative hypotheses can be one-tailed or two-tailed . A two -tailed alternative hypothesis means, for example, that the population mean could be either smaller or larger than what is claimed. A one -tailed alternative hypothesis admits only one of those two possibilities. Most (but not all) hypothesis tests are two-tailed.

The decision about whether the alternative hypothesis is one- or two-tailed is made by reading the RQ ( not by looking at the data). Indeed, the RQ and hypotheses should (in principle) be formed before the data are obtained , or at least before looking at the data if the data are already collected.

The ideas are the same whether the alternative hypothesis is one- or two-tailed: based on the data and the sample statistic, a decision is to be made about whether the alternative hypotheses is supported by the data.

Example 28.1 (Alternative hypotheses) For the body-temperature study, the alternative hypothesis is two-tailed : The RQ asks if the population mean is \(37.0^\circ\text{C}\) or not . That is, two possibilities are considered: that \(\mu\) could be either larger or smaller than \(37.0^\circ\text{C}\) .

Important points about forming hypotheses:

• Hypotheses always concern a population parameter.
• Null hypotheses always contain an ‘equals.’
• Alternative hypothesis are one-tailed or two-tailed, depending on the RQ.
• Hypotheses emerge from the RQ (not the data): The RQ and the hypotheses could be written down before collecting the data.

Assumption vs. Hypothesis

What's the difference.

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

Further Detail

Introduction.

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

Assumptions

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

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

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

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

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

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

Comparison of Attributes

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

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

2. Verifiability

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

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

4. Role in Knowledge Acquisition

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

5. Testability

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

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

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

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

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

Delimitations

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

Limitations

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

Assumptions

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

Click here to cancel reply.

You must be logged in to post a comment.

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

• Contact sales (+234) 08132546417
• Have a questions? [email protected]
• Latest Projects

Project Materials

Tips on making assumptions in a research paper.

Click Here to Download Now.

Do You Have New or Fresh Topic? Send Us Your Topic

How to make assumptions in a research paper.

In the academic environment, making assumptions is vital as the research statement of the problem when writing a project dissertation. Assumptions in an essay are those statements your audience will take as true or false. Today, we will be looking at making assumptions in research writing and errors to be avoided during this process.

What is assumption?

In academic writing, an assumption is regarded as unexamined belief; that is what we are considering without realizing it. Inarguably all research works conclude based on the assumption that the authors have not critically examined.

The Importance of Assumptions in a Thesis

Deciding what assumptions might arise in your readers’ minds is one of the primary functions to be carried out when writing a research paper. Without a doubt, assumptions are the foundation of any credible and valid research work. In fact, without assumptions, research problems cannot be found as they determine the conclusions that would be gotten from your research work.

Identifying Assumptions

It is essential to point out that the type of assumption will determine the conclusion gotten from the research. For this reason, you should critically consider the kinds of assumptions you make in your research. What then makes a proper assumption? Being able to be verified and justified. To give a reasonable assumption, you must not just state, but explain and cite examples to justify your premise’s validity. On the other hand, a wrong assumption is not easily valid and justified. Take, for instance, in case you are assuming that participants will provide honest answers to questions you ask them, explain how the data was gotten, and steps you will take to ensure their identity is protected to guarantee truthfulness.

Assumptions and Hypotheses: Similarities and differences

Many people tend to mix up an assumption with a hypothesis. Although these two concepts share specific characteristics, they are quite different. Below we list two significant similarities and differences between an assumption and thesis.

Similarities between assumption and thesis:

1. Both assumption and hypothesis can be proved and disapproved during the course of the research.

2. Like thesis an assumption must always be affirmative, never a question.

Differences between an assumption and hypothesis:

1. Unlike an assumption, the researcher consciously works towards proving the validity of the hypothesis used for the research.

2.The research work begins based on an assumption, whereas a theory is a goal the study aims to achieve.

Having differentiated between these two concepts, the question now evolves in many writers’ minds, what then is a premise in research?

Is Premise and Assumption the same?

A premise is commonly described as the assumption that the arguments depend on ”fly.” In essence, we are saying that an assumption is sometimes referred to as a premise of research work. Let’s check out the example below to understand better:

1. All men are mortal;

2. Socrates is a man;

3. Therefore, Socrates is mortal.

From the above example, it is evident that the first two assertions are premises. Why are they assumptions? Because there is no attempt to prove their validity, everyone just accepts them as reality. However, the last statement depends on the first two sentences; if those are untrue, it is also inaccurate and vice versa.

Types of Assumptions

There are two types of Assumptions when writing a research paper: directly stated assumption (explicit) or indirectly stated but implied (Implicitly). So immediately, you pinpoint an assumption in research work, watch out for the two types.

Often, to make an efficient reading, it is necessary to go beyond what has been said, that is, read between the lines.

For example, observe this statement:

Patricia stopped drinking soda The explicit assumption is, “Patricia stopped drinking soda.” The implicit assumption is, “Patricia used to drink soda before.”

Now, see this other example:

Fortunately, Patricia stopped drinking soda

The explicit assumption is, “Patricia stopped drinking soda.” The word “fortunately” indicates that the speaker has a positive opinion of the fact – that is the implicit assumption.

Common Assumptions in Research

Arguably, perhaps the most frequent assumption in any research is around the participants’ sincerity when answering the questions being asked. It is important to note that if the questions you ask the respondents are quite sensitive, it is best to assume plausible honesty when compared to answering impersonal questions. If there is element of subjectivity and compromise in the answer being provided, it should be listed as a limitation of the research, not an assumption. Limitations and assumptions of the study should not be in contrast to each other.

Another widespread assumption is the similarity of participants’ characteristics within the study. Another common assumption in research is determining the level of representation a sample size is for a population.

Four Ways to Deal with Assumptions

Like we earlier mentioned, regardless of the type of research being carried out, assumptions are vital to its success. Despite the critical role it plays in research writing when you re-evaluate the assumptions you have made, sometimes you feel like they are not accurate enough; hence you want to change the assumption. Below we have highlighted four tips on how to deal with assumptions in research writing.

1. Don’t touch them, leave them as they are;

When you see the assumptions, you have made in your research, you may think about leaving them. However, your confidence will be boosted about choosing not to touch them if carefully review them and the options available.

2. Explain them in more detail (make them explicit)

Indeed when you make an assumption, you will likely feel like that is the right thing to do; however, your research work will be more understood if you expound more about the assumption, although you don’t need to give examples to back it up.

3. Offer evidence (convert them into supported claims)

We know at this point; you are worried about the fact that we are asking you to provide evidence. Nevertheless, it is something you should consider if you think your audience will probably not agree with one of the assumptions you have made with an example to back it up. So, in this situation, it is ideal for you to turn your assumption into a claim that has proof.

4. Change them (revise the larger claim)

In certain situations, even you are not convinced by the assumption you are presenting to your audience even after several attempts to prove. In this case, the best thing to do is to review the assumption and the statement it serves as a backbone.

Three Common Mistakes about assumptions

When evaluating an assumption, there are inevitable mistakes to be careful of:

Mistake #1: The assumption is terrible because there is no evidence

Many people make a mistake of saying that when an assumption does not have proof, it will fail. However, if you look at the definition of assumption, you will notice that lack of evidence pops out.

Mistake #2: I can’t entirely agree because we cannot know if it’s true or not

Another common mistake about assumption is that if we cannot know whether it is true or false, we cannot say it is an assumption because there is no room for agreeing or disagreeing. But the reality is that even if we cannot ascertain the assumption, we can make an educated guess and explain the reasons for making the assumption.

Mistake #3: The assumption is reasonable because there is evidence

A lot of people express that when there is proof for an assumption, it is a good one. However, the truth is, when your supposed assumption has evidence, and the author tries to prove it, it is no longer an assumption.

From the above, it is evident that assumption is an integral part of research writing. We believe you can now identify what it is and make assumptions to back up your research.

Not What You Were Looking For? Send Us Your Topic

INSTRUCTIONS AFTER PAYMENT

• 1.Your Full name
• 2. Your Active Email Address
• 3. Your Phone Number
• 4. Amount Paid
• 5. Project Topic
• 6. Location you made payment from

» Send the above details to our email; [email protected] or to our support phone number; (+234) 0813 2546 417 . As soon as details are sent and payment is confirmed, your project will be delivered to you within minutes.

Latest Updates

Marketing strategy for an e-business, effects of it in marketing of communication service, effects of advertising on the small and medium enterprises, leave a reply cancel reply.

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

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

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Advertisements

• Hire A Writer
• Plagiarism Research Clinic
• International Students
• Project Categories
• WHY HIRE A PREMIUM RESEARCHER?
• UPGRADE PLAN
• PROFESSIONAL PLAN
• STANDARD PLAN
• MBA MSC STANDARD PLAN
• MBA MSC PROFESSIONAL PLAN

UX Research: Objectives, Assumptions, and Hypothesis

by Rick Dzekman

An often neglected step in UX research

Introduction

UX research should always be done for a clear purpose – otherwise you’re wasting the both your time and the time of your participants. But many people who do UX research fail to properly articulate the purpose in their research objectives. A major issue is that the research objectives include assumptions that have not been properly defined.

When planning UX research you have some goal in mind:

• For generative research it’s usually to find out something about users or customers that you previously did not know
• For evaluative research it’s usually to identify any potential issues in a solution

As part of this goal you write down research objectives that help you achieve that goal. But for many researchers (especially more junior ones) they are missing some key steps:

• How will those research objectives help to reach that goal?
• What assumptions have you made that are necessary for those objectives to reach that goal?
• How does your research (questions, tasks, observations, etc.) help meet those objectives?
• What kind of responses or observations do you need from your participants to meet those objectives?

One approach people use is to write their objectives in the form of research hypothesis. There are a lot of problems when trying to validate a hypothesis with qualitative research and sometimes even with quantitative.

This article focuses largely on qualitative research: interviews, user tests, diary studies, ethnographic research, etc. With qualitative research in mind let’s start by taking a look at a few examples of UX research hypothesis and how they may be problematic.

Research hypothesis

Example hypothesis: users want to be able to filter products by colour.

At first it may seem that there are a number of ways to test this hypothesis with qualitative research. For example we might:

• Observe users shopping on sites with and without colour filters and see whether or not they use them
• Ask users who are interested in our products about how narrow down their choices
• Run a diary study where participants document the ways they narrowed down their searches on various stores
• Make a prototype with colour filters and see if participants use them unprompted

These approaches are all effective but they do not and cannot prove or disprove our hypothesis. It’s not that the research methods are ineffective it’s that the hypothesis itself is poorly expressed.

The first problem is that there are hidden assumptions made by this hypothesis. Presumably we would be doing this research to decide between a choice of possible filters we could implement. But there’s no obvious link between users wanting to filter by colour and a benefit from us implementing a colour filter. Users may say they want it but how will that actually benefit their experience?

The second problem with this hypothesis is that we’re asking a question about “users” in general. How many users would have to want colour filters before we could say that this hypothesis is true?

Example Hypothesis: Adding a colour filter would make it easier for users to find the right products

This is an obvious improvement to the first example but it still has problems. We could of course identify further assumptions but that will be true of pretty much any hypothesis. The problem again comes from speaking about users in general.

Perhaps if we add the ability to filter by colour it might make the possible filters crowded and make it more difficult for users who don’t need colour to find the filter that they do need. Perhaps there is a sample bias in our research participants that does not apply broadly to our user base.

It is difficult (though not impossible) to design research that could prove or disprove this hypothesis. Any such research would have to be quantitative in nature. And we would have to spend time mapping out what it means for something to be “easier” or what “the right products” are.

Example Hypothesis: Travelers book flights before they book their hotels

The problem with this hypothesis should now be obvious: what would it actually mean for this hypothesis to be proved or disproved? What portion of travelers would need to book their flights first for us to consider this true?

Example Hypothesis: Most users who come to our app know where and when they want to fly

This hypothesis is better because it talks about “most users” rather than users in general. “Most” would need to be better defined but at least this hypothesis is possible to prove or disprove.

We could address this hypothesis with quantitative research. If we found out that it was true we could focus our design around the primary use case or do further research about how to attract users at different stages of their journey.

However there is no clear way to prove or disprove this hypothesis with qualitative research. If the app has a million users and 15/20 research participants tell you that this is true would your findings generalise to the entire user base? The margin of error on that finding is 20-25%, meaning that the true results could be closer to 50% or even 100% depending on how unlucky you are with your sample.

Example Hypothesis: Customers want their bank to help them build better savings habits

There are many things wrong with this hypothesis but we will focus on the hidden assumptions and the links to design decisions. Two big assumptions are that (1) it’s possible to find out what research participants want and (2) people’s wants should dictate what features or services to provide.

Research objectives

One of the biggest problem with using hypotheses is that they set the wrong expectations about what your research results are telling you. In Thinking, Fast and Slow, Daniel Kahneman points out that:

• “extreme outcomes (both high and low) are more likely to be found in small than in large samples”
• “the prominence of causal intuitions is a recurrent theme in this book because people are prone to apply causal thinking inappropriately, to situations that require statistical reasoning”
• “when people believe a conclusion is true, they are also very likely to believe arguments that appear to support it, even when these arguments are unsound”

Using a research hypothesis primes us to think that we have found some fundamental truth about user behaviour from our qualitative research. This leads to overconfidence about what the research is saying and to poor quality research that could simply have been skipped in exchange for simply making assumption. To once again quote Kahneman: “you do not believe that these results apply to you because they correspond to nothing in your subjective experience”.

We can fix these problems by instead putting our focus on research objectives. We pay attention to the reason that we are doing the research and work to understand if the results we get could help us with our objectives.

This does not get us off the hook however because we can still create poor research objectives.

Let’s look back at one of our prior hypothesis examples and try to find effective research objectives instead.

Example objectives: deciding on filters

In thinking about the colour filter we might imagine that this fits into a larger project where we are trying to decide what filters we should implement. This is decidedly different research to trying to decide what order to implement filters in or understand how they should work. In this case perhaps we have limited resources and just want to decide what to implement first.

A good approach would be quantitative research designed to produce some sort of ranking. But we should not dismiss qualitative research for this particular project – provided our assumptions are well defined.

Let’s consider this research objective: Understand how users might map their needs against the products that we offer . There are three key aspects to this objective:

• “Understand” is a common form of research objective and is a way that qualitative research can discover things that we cannot find with quant. If we don’t yet understand some user attitude or behaviour we cannot quantify it. By focusing our objective on understanding we are looking at uncovering unknowns.
• By using the word “might” we are not definitively stating that our research will reveal all of the ways that users think about their needs.
• Our focus is on understanding the users’ mental models. Then we are not designing for what users say that they want and we aren’t even designing for existing behaviour. Instead we are designing for some underlying need.

The next step is to look at the assumptions that we are making. One assumption is that mental models are roughly the same between most people. So even though different users may have different problems that for the most part people tend to think about solving problems with the same mental machinery. As we do more research we might discover that this assumption is not true and there are distinctly different kinds of behaviours. Perhaps we know what those are in advance and we can recruit our research participants in a way that covers those distinct behaviours.

Another assumption is that if we understand our users’ mental models that we will be able to design a solution that will work for most people. There are of course more assumptions we could map but this is a good start.

Now let’s look at another research objective: Understand why users choose particular filters . Again we are looking to understand something that we did not know before.

Perhaps we have some prior research that tells us what the biggest pain points are that our products solve. If we have an understanding of why certain filters are used we can think about how those motivations fit in with our existing knowledge.

Mapping objectives to our research plan

Our actual research will involve some form of asking questions and/or making observations. It’s important that we don’t simply forget about our research objectives and start writing questions. This leads to completing research and realising that you haven’t captured anything about some specific objective.

An important step is to explicitly write down all the assumptions that we are making in our research and to update those assumptions as we write our questions or instructions. These assumptions will help us frame our research plan and make sure that we are actually learning the things that we think we are learning. Consider even high level assumptions such as: a solution we design with these insights will lead to a better experience, or that a better experience is necessarily better for the user.

Once we have our main assumptions defined the next step is to break our research objective down further.

Breaking down our objectives

The best way to consider this breakdown is to think about what things we could learn that would contribute to meeting our research objective. Let’s consider one of the previous examples: Understand how users might map their needs against the products that we offer

We may have an assumption that users do in fact have some mental representation of their needs that align with the products they might purchase. An aspect of this research objective is to understand whether or not this true. So two sub-objectives may be to (1) understand why users actually buy these sorts of products (if at all), and (2) understand how users go about choosing which product to buy.

Next we might want to understand what our users needs actually are or if we already have research about this understand which particular needs apply to our research participants and why.

And finally we would want to understand what factors go into addressing a particular need. We may leave this open ended or even show participants attributes of the products and ask which ones address those needs and why.

Once we have a list of sub-objectives we could continue to drill down until we feel we’ve exhausted all the nuances. If we’re happy with our objectives the next step is to think about what responses (or observations) we would need in order to answer those objectives.

It’s still important that we ask open ended questions and see what our participants say unprompted. But we also don’t want our research to be so open that we never actually make any progress on our research objectives.

Reviewing our objectives and pilot studies

At the end it’s important to review every task, question, scenario, etc. and seeing which research objectives are being addressed. This is vital to make sure that your planning is worthwhile and that you haven’t missed anything.

If there’s time it’s also useful to run a pilot study and analyse the responses to see if they help to address your objectives.

Plan accordingly

It should be easy to see why research hypothesis are not suitable for most qualitative research. While it is possible to create suitable hypothesis it is more often than not going to lead to poor quality research. This is because hypothesis create the impression that qualitative research can find things that generalise to the entire user base. In general this is not true for the sample sizes typically used for qualitative research and also generally not the reason that we do qualitative research in the first place.

Instead we should focus on producing effective research objectives and making sure every part of our research plan maps to a suitable objective.