research problem and hypothesis

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

The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

You might also like

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

Literature Review and Theoretical Framework: Understanding the Differences

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

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, 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.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• The Research Problem/Question
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE:   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

• << Previous: Background Information
• Next: Theoretical Framework >>
• Last Updated: Jun 18, 2024 10:45 AM
• URL: https://libguides.usc.edu/writingguide

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

• Privacy Policy

Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Significance of the Study – Examples and Writing...

Delimitations in Research – Types, Examples and...

Research Techniques – Methods, Types and Examples

What is a Hypothesis – Types, Examples and...

Data Collection – Methods Types and Examples

Problem Statement – Writing Guide, Examples and...

The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6047 Accesses

A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

Leedy Paul D. Practical research. Planning and design. 2nd ed. New York: Macmillan; 1960.

Bernard C. Introduction to the study of experimental medicine. New York: Dover; 1957.

Erren TC. The quest for questions—on the logical force of science. Med Hypotheses. 2004;62:635–40.

Article   PubMed   Google Scholar  

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 7. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1966.

Aristotle. The complete works of Aristotle: the revised Oxford Translation. In: Barnes J, editor. vol. 2. Princeton/New Jersey: Princeton University Press; 1984.

Polit D, Beck CT. Conceptualizing a study to generate evidence for nursing. In: Polit D, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. 8th ed. Philadelphia: Wolters Kluwer/Lippincott Williams and Wilkins; 2008. Chapter 4.

Jenicek M, Hitchcock DL. Evidence-based practice. Logic and critical thinking in medicine. Chicago: AMA Press; 2005.

Bacon F. The novum organon or a true guide to the interpretation of nature. A new translation by the Rev G.W. Kitchin. Oxford: The University Press; 1855.

Popper KR. Objective knowledge: an evolutionary approach (revised edition). New York: Oxford University Press; 1979.

Morgan AJ, Parker S. Translational mini-review series on vaccines: the Edward Jenner Museum and the history of vaccination. Clin Exp Immunol. 2007;147:389–94.

Article   PubMed   CAS   Google Scholar  

Pead PJ. Benjamin Jesty: new light in the dawn of vaccination. Lancet. 2003;362:2104–9.

Lee JA. The scientific endeavor: a primer on scientific principles and practice. San Francisco: Addison-Wesley Longman; 2000.

Allchin D. Lawson’s shoehorn, or should the philosophy of science be rated, ‘X’? Science and Education. 2003;12:315–29.

Article   Google Scholar  

Lawson AE. What is the role of induction and deduction in reasoning and scientific inquiry? J Res Sci Teach. 2005;42:716–40.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 2. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Bonfantini MA, Proni G. To guess or not to guess? In: Eco U, Sebeok T, editors. The sign of three: Dupin, Holmes, Peirce. Bloomington: Indiana University Press; 1983. Chapter 5.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 5. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Flach PA, Kakas AC. Abductive and inductive reasoning: background issues. In: Flach PA, Kakas AC, ­editors. Abduction and induction. Essays on their relation and integration. The Netherlands: Klewer; 2000. Chapter 1.

Murray JF. Voltaire, Walpole and Pasteur: variations on the theme of discovery. Am J Respir Crit Care Med. 2005;172:423–6.

Danemark B, Ekstrom M, Jakobsen L, Karlsson JC. Methodological implications, generalization, scientific inference, models (Part II) In: explaining society. Critical realism in the social sciences. New York: Routledge; 2002.

Pasteur L. Inaugural lecture as professor and dean of the faculty of sciences. In: Peterson H, editor. A treasury of the world’s greatest speeches. Douai, France: University of Lille 7 Dec 1954.

Swineburne R. Simplicity as evidence for truth. Milwaukee: Marquette University Press; 1997.

Sakar S, editor. Logical empiricism at its peak: Schlick, Carnap and Neurath. New York: Garland; 1996.

Popper K. The logic of scientific discovery. New York: Basic Books; 1959. 1934, trans. 1959.

Caws P. The philosophy of science. Princeton: D. Van Nostrand Company; 1965.

Popper K. Conjectures and refutations. The growth of scientific knowledge. 4th ed. London: Routledge and Keegan Paul; 1972.

Feyerabend PK. Against method, outline of an anarchistic theory of knowledge. London, UK: Verso; 1978.

Smith PG. Popper: conjectures and refutations (Chapter IV). In: Theory and reality: an introduction to the philosophy of science. Chicago: University of Chicago Press; 2003.

Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5:7–11.

Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiological research. Principles and quantitative methods. New York: Van Nostrand Reinhold; 1982.

Fortune AE, Reid WJ. Research in social work. 3rd ed. New York: Columbia University Press; 1999.

Kerlinger FN. Foundations of behavioral research. 1st ed. New York: Hold, Reinhart and Winston; 1970.

Hoskins CN, Mariano C. Research in nursing and health. Understanding and using quantitative and qualitative methods. New York: Springer; 2004.

Tuckman BW. Conducting educational research. New York: Harcourt, Brace, Jovanovich; 1972.

Wang C, Chiari PC, Weihrauch D, Krolikowski JG, Warltier DC, Kersten JR, Pratt Jr PF, Pagel PS. Gender-specificity of delayed preconditioning by isoflurane in rabbits: potential role of endothelial nitric oxide synthase. Anesth Analg. 2006;103:274–80.

Beyer ME, Slesak G, Nerz S, Kazmaier S, Hoffmeister HM. Effects of endothelin-1 and IRL 1620 on myocardial contractility and myocardial energy metabolism. J Cardiovasc Pharmacol. 1995;26(Suppl 3):S150–2.

PubMed   CAS   Google Scholar  

Stone J, Sharpe M. Amnesia for childhood in patients with unexplained neurological symptoms. J Neurol Neurosurg Psychiatry. 2002;72:416–7.

Naughton BJ, Moran M, Ghaly Y, Michalakes C. Computer tomography scanning and delirium in elder patients. Acad Emerg Med. 1997;4:1107–10.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. 1991;337:867–72.

Stern JM, Simes RJ. Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ. 1997;315:640–5.

Stevens SS. On the theory of scales and measurement. Science. 1946;103:677–80.

Knapp TR. Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nurs Res. 1990;39:121–3.

The Cochrane Collaboration. Open Learning Material. www.cochrane-net.org/openlearning/html/mod14-3.htm . Accessed 12 Oct 2009.

MacCorquodale K, Meehl PE. On a distinction between hypothetical constructs and intervening ­variables. Psychol Rev. 1948;55:95–107.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: ­conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Williamson GM, Schultz R. Activity restriction mediates the association between pain and depressed affect: a study of younger and older adult cancer patients. Psychol Aging. 1995;10:369–78.

Song M, Lee EO. Development of a functional capacity model for the elderly. Res Nurs Health. 1998;21:189–98.

MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.

Download references

Author information

Authors and affiliations.

Department of Medicine, College of Medicine, SUNY Downstate Medical Center, 450 Clarkson Avenue, 1199, Brooklyn, NY, 11203, USA

Phyllis G. Supino EdD

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Phyllis G. Supino EdD .

Editor information

Editors and affiliations.

, Cardiovascular Medicine, SUNY Downstate Medical Center, Clarkson Avenue, box 1199 450, Brooklyn, 11203, USA

Phyllis G. Supino

, Cardiovascualr Medicine, SUNY Downstate Medical Center, Clarkson Avenue 450, Brooklyn, 11203, USA

Jeffrey S. Borer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

Download citation

DOI : https://doi.org/10.1007/978-1-4614-3360-6_3

Published : 18 April 2012

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-3359-0

Online ISBN : 978-1-4614-3360-6

eBook Packages : Medicine Medicine (R0)

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Educational resources and simple solutions for your research journey

What is a Research Problem? Characteristics, Types, and Examples

A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets the problem into a particular context, and defines the relevant parameters, providing the framework for reporting the findings. Therein lies the importance of research problem s.  

The formulation of well-defined research questions is central to addressing a research problem . A research question is a statement made in a question form to provide focus, clarity, and structure to the research endeavor. This helps the researcher design methodologies, collect data, and analyze results in a systematic and coherent manner. A study may have one or more research questions depending on the nature of the study.   

Identifying and addressing a research problem is very important. By starting with a pertinent problem , a scholar can contribute to the accumulation of evidence-based insights, solutions, and scientific progress, thereby advancing the frontier of research. Moreover, the process of formulating research problems and posing pertinent research questions cultivates critical thinking and hones problem-solving skills.   

Table of Contents

What is a Research Problem ?  

Before you conceive of your project, you need to ask yourself “ What is a research problem ?” A research problem definition can be broadly put forward as the primary statement of a knowledge gap or a fundamental challenge in a field, which forms the foundation for research. Conversely, the findings from a research investigation provide solutions to the problem .  

A research problem guides the selection of approaches and methodologies, data collection, and interpretation of results to find answers or solutions. A well-defined problem determines the generation of valuable insights and contributions to the broader intellectual discourse.  

Characteristics of a Research Problem  

Knowing the characteristics of a research problem is instrumental in formulating a research inquiry; take a look at the five key characteristics below:  

Novel : An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.   

Significant : A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.  

Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem . These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.  

Clear and specific : A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.  

Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.

Types of Research Problems  

Across fields and disciplines, there are different types of research problems . We can broadly categorize them into three types.  

• Theoretical research problems

Theoretical research problems deal with conceptual and intellectual inquiries that may not involve empirical data collection but instead seek to advance our understanding of complex concepts, theories, and phenomena within their respective disciplines. For example, in the social sciences, research problem s may be casuist (relating to the determination of right and wrong in questions of conduct or conscience), difference (comparing or contrasting two or more phenomena), descriptive (aims to describe a situation or state), or relational (investigating characteristics that are related in some way).  

Here are some theoretical research problem examples :   

• Ethical frameworks that can provide coherent justifications for artificial intelligence and machine learning algorithms, especially in contexts involving autonomous decision-making and moral agency.  
• Determining how mathematical models can elucidate the gradual development of complex traits, such as intricate anatomical structures or elaborate behaviors, through successive generations.  
• Applied research problems

Applied or practical research problems focus on addressing real-world challenges and generating practical solutions to improve various aspects of society, technology, health, and the environment.  

Here are some applied research problem examples :   

• Studying the use of precision agriculture techniques to optimize crop yield and minimize resource waste.  
• Designing a more energy-efficient and sustainable transportation system for a city to reduce carbon emissions.  
• Action research problems

Action research problems aim to create positive change within specific contexts by involving stakeholders, implementing interventions, and evaluating outcomes in a collaborative manner.  

Here are some action research problem examples :   

• Partnering with healthcare professionals to identify barriers to patient adherence to medication regimens and devising interventions to address them.  
• Collaborating with a nonprofit organization to evaluate the effectiveness of their programs aimed at providing job training for underserved populations.  

These different types of research problems may give you some ideas when you plan on developing your own.  

How to Define a Research Problem  

You might now ask “ How to define a research problem ?” These are the general steps to follow:   

• Look for a broad problem area: Identify under-explored aspects or areas of concern, or a controversy in your topic of interest. Evaluate the significance of addressing the problem in terms of its potential contribution to the field, practical applications, or theoretical insights.
• Learn more about the problem: Read the literature, starting from historical aspects to the current status and latest updates. Rely on reputable evidence and data. Be sure to consult researchers who work in the relevant field, mentors, and peers. Do not ignore the gray literature on the subject.
• Identify the relevant variables and how they are related: Consider which variables are most important to the study and will help answer the research question. Once this is done, you will need to determine the relationships between these variables and how these relationships affect the research problem . 
• Think of practical aspects : Deliberate on ways that your study can be practical and feasible in terms of time and resources. Discuss practical aspects with researchers in the field and be open to revising the problem based on feedback. Refine the scope of the research problem to make it manageable and specific; consider the resources available, time constraints, and feasibility.
• Formulate the problem statement: Craft a concise problem statement that outlines the specific issue, its relevance, and why it needs further investigation.
• Stick to plans, but be flexible: When defining the problem , plan ahead but adhere to your budget and timeline. At the same time, consider all possibilities and ensure that the problem and question can be modified if needed.

Key Takeaways  

• A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.   
• The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.  
• Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.  
• Different types of research problems include theoretical, applied, and action research problems , and these depend on the discipline and nature of the study.  
• An ideal problem is original, important, feasible, specific, and based on evidence.  

Frequently Asked Questions  

Why is it important to define a research problem?  

Identifying potential issues and gaps as research problems is important for choosing a relevant topic and for determining a well-defined course of one’s research. Pinpointing a problem and formulating research questions can help researchers build their critical thinking, curiosity, and problem-solving abilities.   

How do I identify a research problem?  

Identifying a research problem involves recognizing gaps in existing knowledge, exploring areas of uncertainty, and assessing the significance of addressing these gaps within a specific field of study. This process often involves thorough literature review, discussions with experts, and considering practical implications.  

Can a research problem change during the research process?  

Yes, a research problem can change during the research process. During the course of an investigation a researcher might discover new perspectives, complexities, or insights that prompt a reevaluation of the initial problem. The scope of the problem, unforeseen or unexpected issues, or other limitations might prompt some tweaks. You should be able to adjust the problem to ensure that the study remains relevant and aligned with the evolving understanding of the subject matter.

How does a research problem relate to research questions or hypotheses?  

A research problem sets the stage for the study. Next, research questions refine the direction of investigation by breaking down the broader research problem into manageable components. Research questions are formulated based on the problem , guiding the investigation’s scope and objectives. The hypothesis provides a testable statement to validate or refute within the research process. All three elements are interconnected and work together to guide the research.  

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

Related Posts

Article Processing Charges: Impact on Open Access Publishing

How Publishers Can Enhance Reader Engagement with R Discovery’s Article Recommendation System

The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

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

You Might Also Like:

I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

Submit a Comment Cancel reply

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

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

• Print Friendly

An official website of the United States government

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

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

• Publications
• Account settings

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

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

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

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

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

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

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

• Thesis Action Plan New
• Academic Project Planner

Literature Navigator

Thesis dialogue blueprint, writing wizard's template, research proposal compass.

• Why students love us
• Why professors love us
• Rebels Blog (Free)
• Why we are different
• All Products
• Coming Soon

Identifying a Research Problem: A Step-by-Step Guide

Identifying a research problem is a crucial first step in the research process, serving as the foundation for all subsequent research activities. This guide provides a comprehensive overview of the steps involved in identifying a research problem, from understanding its essence to employing advanced strategies for refinement.

Key Takeaways

• Understanding the definition and importance of a research problem is essential for academic success.
• Exploring diverse sources such as literature reviews and consultations can help in formulating a solid research problem.
• A clear problem statement, aligned research objectives, and well-defined questions are crucial for a focused study.
• Evaluating the feasibility and potential impact of a research problem ensures its relevance and scope.
• Advanced strategies, including interdisciplinary approaches and technology utilization, can enhance the identification and refinement of research problems.

Understanding the Essence of Identifying a Research Problem

Defining the research problem.

A research problem is the focal point of any academic inquiry. It is a concise and well-defined statement that outlines the specific issue or question that the research aims to address. This research problem usually sets the tone for the entire study and provides you, the researcher, with a clear purpose and a clear direction on how to go about conducting your research.

Importance in Academic Research

It also demonstrates the significance of your research and its potential to contribute new knowledge to the existing body of literature in the world. A compelling research problem not only captivates the attention of your peers but also lays the foundation for impactful and meaningful research outcomes.

Initial Steps to Identification

To identify a research problem, you need a systematic approach and a deep understanding of the subject area. Below are some steps to guide you in this process:

• Conduct a thorough literature review to understand what has been studied before.
• Identify gaps in the existing research that could form the basis of your study.
• Consult with academic mentors to refine your ideas and approach.

Exploring Sources for Research Problem Identification

Literature review.

When you embark on the journey of identifying a research problem, a thorough literature review is indispensable. This process involves scrutinizing existing research to find literature gaps and unexplored areas that could form the basis of your research. It's crucial to analyze recent studies, seminal works, and review articles to ensure a comprehensive understanding of the topic.

Existing Theories and Frameworks

The exploration of existing theories and frameworks provides a solid foundation for developing a research problem. By understanding the established models and theories, you can identify inconsistencies or areas lacking in depth which might offer fruitful avenues for research.

Consultation with Academic Mentors

Engaging with academic mentors is vital in shaping a well-defined research problem. Their expertise can guide you through the complexities of your field, offering insights into feasible research questions and helping you refine your focus. This interaction often leads to the identification of unique and significant research opportunities that align with current academic and industry trends.

Formulating the Research Problem

Crafting a clear problem statement.

To effectively address your research problem, start by crafting a clear problem statement . This involves succinctly describing who is affected by the problem, why it is important, and how your research will contribute to solving it. Ensure your problem statement is concise and specific to guide the entire research process.

Setting Research Objectives

Setting clear research objectives is crucial for maintaining focus throughout your study. These objectives should directly align with the problem statement and guide your research activities. Consider using a bulleted list to outline your main objectives:

• Understand the underlying factors contributing to the problem
• Explore potential solutions
• Evaluate the effectiveness of proposed solutions

Determining Research Questions

The formulation of precise research questions is a pivotal step in defining the scope and direction of your study. These questions should be directly derived from your research objectives and designed to be answerable through your chosen research methods. Crafting well-defined research questions will help you maintain a clear focus and avoid common pitfalls in the research process.

Evaluating the Scope and Relevance of the Research Problem

Feasibility assessment.

Before you finalize a research problem, it is crucial to assess its feasibility. Consider the availability of resources, time, and expertise required to conduct the research. Evaluate potential constraints and determine if the research problem can be realistically tackled within the given limitations.

Significance to the Field

Ensure that your research problem has a clear and direct impact on the field. It should aim to contribute to existing knowledge and address a real-world issue that is relevant to your academic discipline.

Potential Impact on Existing Knowledge

The potential impact of your research problem on existing knowledge cannot be understated. It should challenge, extend, or refine current understanding in a meaningful way. Consider how your research can add value to the existing body of work and potentially lead to significant advancements in your field.

Techniques for Refining the Research Problem

Narrowing down the focus.

To effectively refine your research problem, start by narrowing down the focus . This involves pinpointing the specific aspects of your topic that are most significant and ensuring that your research problem is not too broad. This targeted approach helps in identifying knowledge gaps and formulating more precise research questions.

Incorporating Feedback

Feedback is crucial in the refinement process. Engage with academic mentors, peers, and experts in your field to gather insights and suggestions. This collaborative feedback can lead to significant improvements in your research problem, making it more robust and relevant.

Iterative Refinement Process

Refinement should be seen as an iterative process, where you continuously refine and revise your research problem based on new information and feedback. This approach ensures that your research problem remains aligned with current trends and academic standards, ultimately enhancing its feasibility and relevance.

Challenges in Identifying a Research Problem

Common pitfalls and how to avoid them.

Identifying a research problem can be fraught with common pitfalls such as selecting a topic that is too broad or too narrow. To avoid these, you should conduct a thorough literature review and seek feedback from peers and mentors. This proactive approach ensures that your research question is both relevant and manageable.

Dealing with Ambiguity

Ambiguity in defining the research problem can lead to significant challenges down the line. Ensure clarity by operationalizing variables and explicitly stating the research objectives. This clarity will guide your entire research process, making it more structured and focused.

Balancing Novelty and Practicality

While it's important to address a novel issue in your research, practicality should not be overlooked. A research problem should not only contribute new knowledge but also be feasible and have clear implications. Balancing these aspects often requires iterative refinement and consultation with academic mentors to align your research with real-world applications.

Advanced Strategies for Identifying a Research Problem

Interdisciplinary approaches.

Embrace the power of interdisciplinary approaches to uncover unique and comprehensive research problems. By integrating knowledge from various disciplines, you can address complex issues that single-field studies might overlook. This method not only broadens the scope of your research but also enhances its applicability and depth.

Utilizing Technology and Data Analytics

Leverage technology and data analytics to refine and identify research problems with precision. Advanced tools like machine learning and big data analysis can reveal patterns and insights that traditional methods might miss. This approach is particularly useful in fields where large datasets are involved, or where real-time data integration can lead to more dynamic research outcomes.

Engaging with Industry and Community Needs

Focus on the needs of industry and community to ensure your research is not only academically sound but also practically relevant. Engaging with real-world problems can provide a rich source of research questions that are directly applicable and beneficial to society. This strategy not only enhances the relevance of your research but also increases its potential for impact.

Dive into the world of academic success with our 'Advanced Strategies for Identifying a Research Problem' at Research Rebels. Our expertly crafted guides and action plans are designed to simplify your thesis journey, transforming complex academic challenges into manageable tasks. Don't wait to take control of your academic future. Visit our website now to learn more and claim your special offer!

In conclusion, identifying a research problem is a foundational step in the academic research process that requires careful consideration and systematic approach. This guide has outlined the essential steps involved, from understanding the context and reviewing existing literature to formulating clear research questions. By adhering to these guidelines, researchers can ensure that their studies are grounded in a well-defined problem, enhancing the relevance and impact of their findings. It is crucial for scholars to approach this task with rigor and critical thinking to contribute meaningfully to the body of knowledge in their respective fields.

Frequently Asked Questions

What is a research problem.

A research problem is a specific issue, inconsistency, or gap in knowledge that needs to be addressed through scientific inquiry. It forms the foundation of a research study, guiding the research questions, methodology, and analysis.

Why is identifying a research problem important?

Identifying a research problem is crucial as it determines the direction and scope of the study. It helps researchers focus their inquiry, formulate hypotheses, and contribute to the existing body of knowledge.

How do I identify a suitable research problem?

To identify a suitable research problem, start with a thorough literature review to understand existing research and identify gaps. Consult with academic mentors, and consider relevance, feasibility, and your own interests.

What are some common pitfalls in identifying a research problem?

Common pitfalls include choosing a problem that is too broad or too narrow, not aligning with existing literature, lack of originality, and failing to consider the practical implications and feasibility of the study.

Can technology help in identifying a research problem?

Yes, technology and data analytics can aid in identifying research problems by providing access to a vast amount of data, revealing patterns and trends that might not be visible otherwise. Tools like digital libraries and research databases are particularly useful.

How can I refine my research problem?

Refine your research problem by narrowing its focus, seeking feedback from peers and mentors, and continually reviewing and adjusting the problem statement based on new information and insights gained during preliminary research.

How Do You Write a Hypothesis? Step-by-Step Instructions

How do you write an hypothesis detailed explanation and examples, how to ask a research question: strategies and tips, how do you write a good hypothesis tips and techniques, how to write a proposal for a thesis: a comprehensive guide.

How Do You Write a Hypothesis for a Research Paper? Step-by-Step Guide

How to Write a Thesis Fast: Tips and Strategies for Success

The Note-Taking Debate: Pros and Cons of Digital and Analog Methods

Maximize Your Academic Excellence with These 9 Evening Habits for Quality Sleep

Stress Less: Boosting Student Well-being with Mindfulness

Thesis Action Plan

• Rebels Blog
• Blog Articles
• Terms and Conditions
• Payment and Shipping Terms
• Privacy Policy
• Return Policy

© 2024 Research Rebels, All rights reserved.

Your cart is currently empty.

Have a language expert improve your writing

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

• Knowledge Base
• Starting the research process
• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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.

Methodology

• 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

Cite this Scribbr article

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

McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/research-process/research-question-examples/

Is this article helpful?

Shona McCombes

Other students also liked, writing strong research questions | criteria & examples, how to choose a dissertation topic | 8 steps to follow, evaluating sources | methods & examples, what is your plagiarism score.

Research Problem

Ai generator.

A research problem is a specific issue or gap in knowledge that a researcher aims to address through systematic investigation. It forms the foundation of a study, guiding the research question, research design , and potential outcomes. Identifying a clear research problem is crucial as it often emerges from existing literature, theoretical frameworks, and practical considerations. In a student case study , the research question and hypothesis stem from the identified research problem.

What is a Research Problem?

A research problem is a specific issue, difficulty, contradiction, or gap in knowledge that a researcher aims to address through systematic investigation. It forms the basis of a study, guiding the research question, research design, and the formulation of a hypothesis.

Examples of Research Problem

• Impact of Social Media on Adolescent Mental Health : Investigating how social media usage affects the mental health and well-being of teenagers.
• Climate Change and Agricultural Productivity : Examining the effects of climate change on crop yields and farming practices.
• Online Learning and Student Engagement : Assessing the effectiveness of online learning platforms in maintaining student engagement and academic performance.
• Healthcare Access in Rural Areas : Exploring the barriers to healthcare access in rural communities and potential solutions.
• Workplace Diversity and Employee Performance : Analyzing how workplace diversity influences team dynamics and employee productivity.
• Renewable Energy Adoption : Studying the factors that influence the adoption of renewable energy sources in urban versus rural areas.
• AI in Healthcare Diagnostics : Evaluating the accuracy and reliability of artificial intelligence in medical diagnostics.
• Gender Disparities in STEM Education : Investigating the causes and consequences of gender disparities in STEM education and careers.
• Urbanization and Housing Affordability : Exploring the impact of rapid urbanization on housing affordability and availability in major cities.
• Public Transportation Efficiency : Assessing the efficiency and effectiveness of public transportation systems in reducing urban traffic congestion.

Research Problem Examples for Students

• The Impact of Homework on Academic Achievement in High School Students
• The Relationship Between Sleep Patterns and Academic Performance in College Students
• The Effects of Extracurricular Activities on Social Skills Development
• Influence of Parental Involvement on Students’ Attitudes Toward Learning
• The Role of Technology in Enhancing Classroom Learning
• Factors Contributing to Student Anxiety During Exams
• The Effectiveness of Peer Tutoring in Improving Reading Skills
• Challenges Faced by International Students in Adapting to New Educational Systems
• Impact of Nutrition on Concentration and Academic Performance
• The Role of Socioeconomic Status in Access to Higher Education Opportunities

Research Problems Examples in Education

• Effect of Class Size on Student Learning Outcomes
• Impact of Technology Integration in Classroom Instruction
• Influence of Teacher Professional Development on Student Achievement
• Challenges in Implementing Inclusive Education for Students with Disabilities
• Effectiveness of Bilingual Education Programs on Language Proficiency
• Role of Parental Involvement in Enhancing Academic Performance
• Impact of School Leadership on Teacher Retention and Job Satisfaction
• Assessment of Remote Learning Efficacy During the COVID-19 Pandemic
• Barriers to STEM Education Participation Among Female Students
• Effect of Socioeconomic Status on Access to Quality Education

Research Problems Examples in Business

• Impact of Employee Engagement on Productivity and Retention
• Effectiveness of Social Media Marketing Strategies on Consumer Behavior
• Challenges in Implementing Sustainable Business Practices
• Influence of Leadership Styles on Organizational Performance
• Role of Corporate Culture in Driving Innovation
• Impact of Remote Work on Team Collaboration and Communication
• Strategies for Managing Supply Chain Disruptions
• Effect of Customer Feedback on Product Development
• Challenges in Expanding into International Markets
• Influence of Brand Loyalty on Customer Retention

Basic Research Problem Examples

• Effect of Sleep on Cognitive Function
• Impact of Exercise on Mental Health
• Influence of Diet on Academic Performance
• Role of Social Support in Stress Management
• Impact of Screen Time on Children’s Behavior
• Effects of Pollution on Public Health
• Influence of Music on Mood and Productivity
• Role of Genetics in Disease Susceptibility
• Impact of Advertising on Consumer Choices
• Effects of Climate Change on Local Wildlife

Research Problem in Research Methodology

A research problem in research methodology refers to an issue or gap in the process of conducting research that requires a solution. Examples include:

• Validity and Reliability of Measurement Tools : Ensuring that instruments used for data collection consistently produce accurate results.
• Selection of Appropriate Sampling Techniques : Determining the best sampling method to ensure the sample represents the population accurately.
• Bias in Data Collection and Analysis : Identifying and minimizing biases that can affect the validity of research findings.
• Ethical Considerations in Research : Addressing ethical issues related to participant consent, confidentiality, and data protection.
• Generalizability of Research Findings : Ensuring that research results are applicable to broader populations beyond the study sample.
• Mixed Methods Research Design : Effectively integrating qualitative and quantitative approaches in a single study.
• Data Interpretation and Reporting : Developing accurate and unbiased interpretations and reports of research findings.
• Longitudinal Study Challenges : Managing the complexities of conducting studies over extended periods.
• Control of Extraneous Variables : Identifying and controlling variables that can affect the dependent variable outside the study’s primary focus.
• Developing Theoretical Frameworks : Constructing robust frameworks that guide the research process and support hypothesis development.

Characteristics of a Research Problem

• Clarity : The research problem should be clearly defined, unambiguous, and understandable to all stakeholders.
• Specificity : It should be specific and narrow enough to be addressed comprehensively within the scope of the research.
• Relevance : The problem should be significant and relevant to the field of study, contributing to the advancement of knowledge or practice.
• Feasibility : It should be practical and manageable, considering the resources, time, and capabilities available to the researcher.
• Novelty : The research problem should address an original question or gap in the existing literature, providing new insights or perspectives.
• Researchability : The problem should be researchable using scientific methods, including data collection, analysis, and interpretation.
• Ethical Considerations : The research problem should be ethically sound, ensuring no harm to participants or the environment.
• Alignment with Objectives : The problem should align with the research objectives and goals, guiding the direction and purpose of the study.
• Measurability : It should be possible to measure and evaluate the outcomes related to the problem using appropriate metrics and methodologies.
• Contextualization : The problem should be placed within a broader context, considering theoretical frameworks, existing literature, and practical applications.

Types of Research Problems

• Aim: To describe the characteristics of a specific phenomenon or population.
• Example: “What are the key features of successful online education programs?”
• Aim: To compare two or more groups, variables, or phenomena.
• Example: “How does employee satisfaction differ between remote and on-site workers?”
• Aim: To determine cause-and-effect relationships between variables.
• Example: “What is the impact of leadership style on employee productivity?”
• Aim: To examine the relationship between two or more variables.
• Example: “What is the relationship between social media usage and self-esteem among teenagers?”
• Aim: To explore a new or under-researched area where little information is available.
• Example: “What are the emerging trends in consumer behavior post-pandemic?”
• Aim: To solve a specific, practical problem faced by an organization or society.
• Example: “How can small businesses improve their cybersecurity measures?”
• Aim: To expand existing theories or develop new theoretical frameworks.
• Example: “How can existing theories of motivation be integrated to better understand employee behavior?”
• Aim: To evaluate the effects of policies or suggest improvements.
• Example: “What are the effects of the new minimum wage laws on small businesses?”
• Aim: To investigate ethical issues within a field or practice.
• Example: “What are the ethical implications of AI in decision-making processes?”
• Aim: To address issues that span multiple disciplines or fields of study.
• Example: “How can principles of environmental science and economics be combined to develop sustainable business practices?”

How to Define a Research Problem

Defining a research problem involves several key steps that help in identifying and articulating a specific issue that needs investigation. Here’s a structured approach:

• Choose a general area of interest or field relevant to your expertise or curiosity. This can be broad initially and will be narrowed down through the next steps.
• Review existing research to understand what has already been studied. This helps in identifying gaps, inconsistencies, or areas that need further exploration.
• Based on your literature review, refine your broad topic to a more specific issue or aspect that has not been adequately addressed.
• Ensure the problem is significant and relevant to the field. It should address a real-world issue or theoretical gap that contributes to advancing knowledge or solving practical problems.
• Clearly articulate the problem in a concise and precise manner. This statement should explain what the problem is, why it is important, and how it impacts the field.
• Develop specific research questions that your study will answer. These questions should be directly related to your problem statement and guide the direction of your research.
• Establish clear research objectives that outline what you aim to achieve. Formulate hypotheses if applicable, which are testable predictions related to your research questions.
• Consider the resources, time, and scope of your study. Ensure that the research problem you have defined is feasible to investigate within the constraints you have.
• Discuss your defined research problem with peers, mentors, or experts in the field. Feedback can help refine and improve your problem statement.

Importance of Research Problem

The research problem is crucial as it forms the foundation of any research study, guiding the direction and focus of the investigation. It helps in:

• Defining Objectives : Clarifies the purpose and objectives of the research, ensuring the study remains focused and relevant.
• Guiding Research Design : Determines the methodology and approach, including data collection and analysis techniques.
• Identifying Significance : Highlights the importance and relevance of the study, demonstrating its potential impact on the field.
• Focusing Efforts : Helps researchers concentrate their efforts on addressing specific issues, leading to more precise and meaningful results.
• Resource Allocation : Assists in the efficient allocation of resources, including time, funding, and manpower, by prioritizing critical aspects of the research.

FAQ’s

Why is defining a research problem important.

Defining a research problem is crucial because it guides the research process, helps focus on specific objectives, and determines the direction of the study.

How do you identify a research problem?

Identify a research problem by reviewing existing literature, considering real-world issues, discussing with experts, and reflecting on personal experiences and observations.

What is the difference between a research problem and a research question?

A research problem identifies the issue to be addressed, while a research question is a specific query the research aims to answer.

Can a research problem change during the study?

Yes, a research problem can evolve as new data and insights emerge, requiring refinement or redefinition to better align with findings.

How do you formulate a research problem?

Formulate a research problem by clearly stating the issue, outlining its significance, and specifying the context and scope of the problem.

What is the role of literature review in identifying a research problem?

A literature review helps identify gaps, inconsistencies, and unresolved issues in existing research, which can guide the formulation of a research problem.

How does a research problem impact the research design?

The research problem shapes the research design by determining the methodology, data collection techniques, and analysis strategies needed to address the issue.

What are common sources of research problems?

Common sources include academic literature, practical experiences, societal issues, technological advancements, and gaps identified in previous research.

How specific should a research problem be?

A research problem should be specific enough to guide focused research but broad enough to allow comprehensive investigation and meaningful results.

How do research objectives relate to the research problem?

Research objectives are specific goals derived from the research problem, detailing what the study aims to achieve and how it plans to address the problem.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 19 June 2024

Language is primarily a tool for communication rather than thought

• Evelina Fedorenko   ORCID: orcid.org/0000-0003-3823-514X 1 , 2 ,
• Steven T. Piantadosi 3 &
• Edward A. F. Gibson 1  

Nature volume  630 ,  pages 575–586 ( 2024 ) Cite this article

9669 Accesses

850 Altmetric

Metrics details

• Human behaviour

Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

The language network as a natural kind within the broader landscape of the human brain

An investigation across 45 languages and 12 language families reveals a universal language network

Two views on the cognitive brain

Barham, L. & Everett, D. Semiotics and the origin of language in the Lower Palaeolithic. J. Archaeol. Method Theory 28 , 535–579 (2021).

Article   Google Scholar  

Hockett, C. F. The origin of speech. Sci. Am. 203 , 88–97 (1960). A classic overview of the relationship between key features of human language and communication systems found in other species, with a focus on distinctive and shared properties .

Jackendoff, R. & Pinker, S. The faculty of language: what’s special about it? Cognition 95 , 201–236 (2005).

Article   PubMed   Google Scholar  

Hurford, J. R. Language in the Light of Evolution: Volume 1, The Origins of Meaning (Oxford Univ. Press, 2007).

Kirby, S., Cornish, H. & Smith, K. Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc. Natl Acad. Sci. USA 105 , 10681–10686 (2008). This behavioural investigation introduces an experimental paradigm based on iterated learning of artificial languages for studying the cultural evolution of language; the findings suggest that languages evolve to maximize their transmissibility by becoming easier to learn and more structured .

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Seyfarth, R. M. & Cheney, D. L. The Social Origins of Language (Princeton Univ. Press, 2018).

Gibson, E. et al. How efficiency shapes human language. Trends Cogn. Sci. 23 , 389–407 (2019).

Chomsky, N. The Minimalist Program (MIT Press, 1995).

Carruthers, P. The cognitive functions of language. Behav. Brain Sci. 25 , 657–674 (2002). This comprehensive review discusses diverse language-for-thought views and puts forward a specific proposal whereby language has a critical role in cross-domain integration .

Gentner, D. & Goldin-Meadow, S. Language in Mind: Advances in the Study of Language and Thought (MIT Press, 2003).

Majid, A., Bowerman, M., Kita, S., Haun, D. B. & Levinson, S. C. Can language restructure cognition? The case for space. Trends Cogn. Sci. 8 , 108–114 (2004).

Vygotsky, L. S. Thought and Language (MIT Press, 2012).

Lupyan, G. The centrality of language in human cognition. Lang. Learn. 66 , 516–553 (2016).

Davidson, D. in Mind and Language (ed. Guttenplan, S.) 1975–1977 (Oxford Univ. Press, 1975).

Dummett, M. Origins of Analytical Philosophy (Harvard Univ. Press, 1994).

Gleitman, L. & Papafragou, A. in The Cambridge Handbook of Thinking and Reasoning (eds Holyoak, K. J. & Morrison, R. G.) 633–661 (Cambridge Univ. Press, 2005).

de Villiers, J. in Understanding Other Minds: Perspectives from Developmental Cognitive Neuroscience (eds Baron-Cohen, S. et al.) 83–123 (Oxford Univ. Press, 2000).

Gentner, D. in Language in Mind: Advances in the Study of Language and Thought (eds Gentner, D. & Goldin-Meadow, S.) 3–14 (MIT Press, 2003). This position piece articulates one version of a language-for-thought hypothesis, whereby human intelligence is due to a combination of our analogical reasoning ability, possession of symbolic representations, and the ability of relational language to improve analogical reasoning abilities .

Buller, D. J. Adapting Minds: Evolutionary Psychology and the Persistent Quest for Human Nature (MIT Press, 2005).

Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8 , 4–15 (1982).

Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27 , 379–423 (1948). This article introduces a formal framework for systems of information transfer, with core concepts such as channel capacity, and lays a foundation for the field of information theory .

Article   MathSciNet   Google Scholar  

Goldberg, A. E. Constructions: A Construction Grammar Approach to Argument Structure (Univ. Chicago Press, 1995).

Jackendoff, R. Foundations of Language: Brain, Meaning, Grammar, Evolution (Oxford Univ. Press, 2002).

Geschwind, N. The organization of language and the brain: language disorders after brain damage help in elucidating the neural basis of verbal behavior. Science 170 , 940–944 (1970).

Article   ADS   CAS   PubMed   Google Scholar  

Friederici, A. D. Towards a neural basis of auditory sentence processing. Trends Cogn. Sci. 6 , 78–84 (2002).

Bates, E. et al. Voxel-based lesion–symptom mapping. Nat. Neurosci. 6 , 448–450 (2003).

Article   CAS   PubMed   Google Scholar  

Hagoort, P. The neurobiology of language beyond single-word processing. Science 366 , 55–58 (2019).

Fedorenko, E., Ivanova, A. I. & Regev, T. I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 25 , 289–312 (2024).

Neville, H. J. et al. Cerebral organization for language in deaf and hearing subjects: biological constraints and effects of experience. Proc. Natl Acad. Sci. USA 95 , 922–929 (1998).

Fedorenko, E., Hsieh, P.-J., Nieto-Castañon, A., Whitfield-Gabrieli, S. & Kanwisher, N. A new method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104 , 1177–1194 (2010).

Article   PubMed   PubMed Central   Google Scholar  

Vagharchakian, L., Dehaene-Lambertz, G., Pallier, C. & Dehaene, S. A temporal bottleneck in the language comprehension network. J. Neurosci. 32 , 9089–9102 (2012).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Regev, M., Honey, C. J., Simony, E. & Hasson, U. Selective and invariant neural responses to spoken and written narratives. J. Neurosci. 33 , 15978–15988 (2013).

Hu, J. et al. Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production. Cereb. Cortex 33 , 4384–4404 (2022).

Article   PubMed Central   Google Scholar  

Menenti, L., Gierhan, S. M. E., Segaert, K. & Hagoort, P. Shared language: overlap and segregation of the neuronal infrastructure for speaking and listening revealed by functional MRI. Psychol. Sci. 22 , 1173–1182 (2011). This fMRI investigation establishes that language comprehension and language production draw on the same brain areas in the left frontal and temporal cortex .

Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: what is it, who has it, and how did it evolve? Science 298 , 1569–1579 (2002).

Pallier, C., Devauchelle, A. D. & Dehaene, S. Cortical representation of the constituent structure of sentences. Proc. Natl Acad. Sci. USA 108 , 2522–2527 (2011).

Bozic, M., Fonteneau, E., Su, L. & Marslen‐Wilson, W. D. Grammatical analysis as a distributed neurobiological function. Hum. Brain Mapp. 36 , 1190–1201 (2015).

Rodd, J. M., Vitello, S., Woollams, A. M. & Adank, P. Localising semantic and syntactic processing in spoken and written language comprehension: an activation likelihood estimation meta-analysis. Brain Lang. 141 , 89–102 (2015).

Blank, I., Balewski, Z., Mahowald, K. & Fedorenko, E. Syntactic processing is distributed across the language system. NeuroImage 127 , 307–323 (2016).

Fedorenko, E. et al. Neural correlate of the construction of sentence meaning. Proc. Natl Acad. Sci. USA 113 , E6256–E6262 (2016).

Nelson, M. J. et al. Neurophysiological dynamics of phrase-structure building during sentence processing. Proc. Natl Acad. Sci. USA 114 , E3669–E3678 (2017).

Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203 , 104348 (2020). This fMRI investigation establishes that every part of the language network that is sensitive to syntactic structure building is also sensitive to word meanings and comprehensively reviews literature relevant to the syntax selectivity debate .

Giglio, L., Ostarek, M. O., Weber, K. & Hagoort, P. Commonalities and asymmetries in the neurobiological infrastructure for language production and comprehension. Cereb. Cortex 32 , 1405–1418 (2022).

Heilbron, M., Armeni, K., Schoffelen, J. M., Hagoort, P. & De Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Proc. Natl Acad. Sci. USA 119 , e2201968119 (2022).

Shain, C., Blank, I. A., Fedorenko, E., Gibson, E. & Schuler, W. Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex. J. Neurosci. 42 , 7412–7430 (2022).

Desbordes, T. et al. Dimensionality and ramping: signatures of sentence integration in the dynamics of brains and deep language models. J. Neurosci. 43 , 5350–5364 (2023).

Shain, C. et al. Distributed sensitivity to syntax and semantics throughout the language network. J. Cogn. Neurosci. 22 , 1–43 (2024). This fMRI investigation establishes distributed sensitivity to cognitive demands associated with lexical access, syntactic structure building and semantic composition across the language network.

Tuckute, G. et al. Driving and suppressing the human language network using large language models. Nat. Hum. Behav. 8 , 544–561 (2024).

Gentner, D. Structure-mapping: a theoretical framework for analogy. Cogn. Sci. 7 , 155–170 (1983).

Google Scholar  

Duncan, J. How Intelligence Happens (Yale Univ. Press, 2012).

Varley, R. A., Klessinger, N. J., Romanowski, C. A. & Siegal, M. Agrammatic but numerate. Proc. Natl Acad. Sci. USA 102 , 3519–3524 (2005). Patients with acquired damage to the language network display aphasia and linguistic deficits (including severe grammatical difficulties) but perform at the level of neurotypical control participants on diverse numerical reasoning tasks .

Klessinger, N., Szczerbinski, M. & Varley, R. Algebra in a man with severe aphasia. Neuropsychologia 45 , 1642–1648 (2007).

Lecours, A. & Joanette, Y. Linguistic and other psychological aspects of paroxysmal aphasia. Brain and Language 10 , 1–23 (1980).

Kertesz, A. in Thought Without Language (ed. Weiskrantz, L.) 451–463 (Oxford Univ. Press, 1988).

Varley, R. & Siegal, M. Evidence for cognition without grammar from causal reasoning and ‘theory of mind’ in an agrammatic aphasic patient. Curr. Biol. 10 , 723–726 (2000).

Siegal, M., Varley, R. & Want, S. C. Mind over grammar: reasoning in aphasia and development. Trends Cogn. Sci. 5 , 296–301 (2001).

Varley, R. In Cognitive Bases of Science (eds Carruthers, P. et al.) 99–116 (Cambridge Univ. Press, 2002).

Woolgar, A., Duncan, J., Manes, F. & Fedorenko, E. Fluid intelligence is supported by the multiple-demand system not the language system. Nat. Hum. Behav. 2 , 200–204 (2018).

Dronkers, N. F., Ludy, C. A. & Redfern, B. B. Pragmatics in the absence of verbal language: descriptions of a severe aphasic and a language-deprived adult. J. Neurolinguistics 11 , 179–190 (1998).

Varley, R., Siegal, M. & Want, S. C. Severe impairment in grammar does not preclude theory of mind. Neurocase 7 , 489–493 (2001).

Apperly, I. A., Samson, D., Carroll, N., Hussain, S. & Humphreys, G. Intact first-and second-order false belief reasoning in a patient with severely impaired grammar. Soc. Neurosci. 1 , 334–348 (2006). A person with acquired damage to the language network and consequent aphasia exhibits linguistic deficits but performs at the level of neurotypical control participants on theory of mind tasks .

Willems, R. M., Benn, Y., Hagoort, P., Toni, I. & Varley, R. Communicating without a functioning language system: Implications for the role of language in mentalizing. Neuropsychologia 49 , 3130–3135 (2011).

Bek, J., Blades, M., Siegal, M. & Varley, R. Language and spatial reorientation: evidence from severe aphasia. J. Exp. Psychol. 36 , 646 (2010).

Caramazza, A., Berndt, R. S. & Brownell, H. H. The semantic deficit hypothesis: Perceptual parsing and object classification by aphasic patients. B. Lang. 15 , 161–189 (1982).

Article   CAS   Google Scholar  

Chertkow, H., Bub, D., Deaudon, C. & Whitehead, V. On the status of object concepts in aphasia. Brain Lang. 58 , 203–232 (1997).

Saygın, A. P., Wilson, S. M., Dronkers, N. F. & Bates, E. Action comprehension in aphasia: linguistic and non-linguistic deficits and their lesion correlates. Neuropsychologia 42 , 1788–1804 (2004).

Jefferies, E. & Lambon Ralph, M. A. Semantic impairment in stroke aphasia versus semantic dementia: a case-series comparison. Brain 129 , 2132–2147 (2006).

Dickey, M. W. & Warren, T. The influence of event-related knowledge on verb-argument processing in aphasia. Neuropsychologia 67 , 63–81 (2015).

Ivanova, A. A. et al. The language network is recruited but not required for nonverbal event semantics. Neurobiol. Lang. 2 , 176–201 (2021). In this fMRI study, semantic processing of event pictures in neurotypical individuals engages the language network, but less than verbal descriptions of the same events; however, individuals with acquired damage to the language network and consequent aphasia perform at the level of neurotypical control participants on a non-verbal semantic task .

Benn, Y. et al. The language network is not engaged in object categorization. Cereb. Cortex 33 , 10380–10400 (2023).

Varley, R. Reason without much language. Lang. Sci. 46 , 232–244 (2014).

Dehaene, S., Spelke, E., Pinel, P., Stanescu, R. & Tsivkin, S. Sources of mathematical thinking: behavioral and brain-imaging evidence. Science 284 , 970–974 (1999).

Hermer, L. & Spelke, E. Modularity and development: the case of spatial reorientation. Cognition 61 , 195–232 (1996).

Lupyan, G. Extracommunicative functions of language: verbal interference causes selective categorization impairments. Psychon. Bull. Rev. 16 , 711–718 (2009).

Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124 , 1415–1448 (2020). This fMRI investigation of the language network establishes this network as one of the intrinsic large-scale networks in the human brain, distinct from nearby cognitive networks .

Fedorenko, E. & Blank, I. A. Broca’s area is not a natural kind. Trends Cogn. Sci. 24 , 270–284 (2020).

Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108 , 16428–16433 (2011). This fMRI investigation finds that arithmetic addition, demanding executive function tasks and music processing do not engage the language areas, thus establishing their selectivity for linguistic input over non-linguistic inputs and tasks .

Monti, M. M., Parsons, L. M. & Osherson, D. N. Thought beyond language: neural dissociation of algebra and natural language. Psychol. Sci. 23 , 914–922 (2012).

Amalric, M. & Dehaene, S. A distinct cortical network for mathematical knowledge in the human brain. NeuroImage 189 , 19–31 (2019).

Monti, M. M., Osherson, D. N., Martinez, M. J. & Parsons, L. M. Functional neuroanatomy of deductive inference: a language-independent distributed network. NeuroImage 37 , 1005–1016 (2007).

Monti, M. M., Parsons, L. M. & Osherson, D. N. The boundaries of language and thought in deductive inference. Proc. Natl Acad. Sci. USA 106 , 12554–12559 (2009). This fMRI investigation finds largely non-overlapping activations of brain regions to language processing and logical processing, thus establishing the selectivity of language areas for linguistic input over logic statements .

Ivanova, A. A. et al. Comprehension of computer code relies primarily on domain-general executive brain regions. eLife 9 , e58906 (2020).

Liu, Y. F., Kim, J., Wilson, C. & Bedny, M. Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network. eLife 9 , e59340 (2020).

Paunov, A. M., Blank, I. A. & Fedorenko, E. Functionally distinct language and theory of mind networks are synchronized at rest and during language comprehension. J. Neurophysiol. 121 , 1244–1265 (2019).

Paunov, A. M. et al. Differential tracking of linguistic vs. mental state content in naturalistic stimuli by language and theory of mind (ToM) brain networks. Neurobiol. Lang. 3 , 413–440 (2022).

Shain, C., Paunov, A., Chen, X., Lipkin, B. & Fedorenko, E. No evidence of theory of mind reasoning in the human language network. Cereb. Cortex 33 , 6299–6319 (2023).

Sueoka, Y., Paunov, A., Ivanova, A., Blank, I. A. & Fedorenko, E. The language network reliably “tracks” naturalistic meaningful non-verbal stimuli. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00135 (2024).

Piaget, J. The Language and Thought of the Child (Harcourt Brace, 1926).

Gentner, D. & Loewenstein, J. in Language, Literacy, and Cognitive Development: The Development and Consequences of Symbolic Communication (eds Amsel, E. & Byrnes, J. P.) 89–126 (Lawrence Erlbaum Associates, 2002).

Appleton, M. & Reddy, V. Teaching three year‐olds to pass false belief tests: a conversational approach. Soc. Dev. 5 , 275–291 (1996).

Slaughter, V. & Gopnik, A. Conceptual coherence in the child’s theory of mind: training children to understand belief. Child Dev. 67 , 2967–2988 (1996).

Hiersche, K. J., Schettini, E., Li, J. & Saygin, Z. M. (2022). Functional dissociation of the language network and other cognition in early childhood. Preprint at bioRxiv https://doi.org/10.1101/2022.08.11.503597 (2023).

Hiersche, K. J. Functional Organization and Modularity of the Superior Temporal Lobe in Children . Masters thesis, The Ohio State University (2023).

Hall, W. C. What you don’t know can hurt you: the risk of language deprivation by impairing sign language development in deaf children. Matern. Child Health J. 21 , 961–965 (2017).

Hall, M. L., Hall, W. C. & Caselli, N. K. Deaf children need language, not (just) speech. First Lang. 39 , 367–395 (2019).

Bedny, M. & Saxe, R. Insights into the origins of knowledge from the cognitive neuroscience of blindness. Cogn. Neuropsychol. 29 , 56–84 (2012).

Grand, G., Blank, I. A., Pereira, F. & Fedorenko, E. Semantic projection recovers rich human knowledge of multiple object features from word embeddings. Nat. Hum. Behav. 6 , 975–987 (2022).

Jackendoff, R. How language helps us think. Pragmat. Cogn. 4 , 1–34 (1996).

Jackendoff. R. The User’s Guide to Meaning (MIT Press, 2012).

Curtiss, S. Genie: A Psycholinguistic Study of a Modern-day Wild Child (Academic Press, 1977).

Peterson, C. C. & Siegal, M. Representing inner worlds: theory of mind in autistic, deaf, and normal hearing children. Psychol. Sci. 10 , 126–129 (1999).

Richardson, H. et al. Reduced neural selectivity for mental states in deaf children with delayed exposure to sign language. Nat. Commun. 11 , 3246 (2020).

Spelke, E. S. What Babies Know: Core Knowledge and Composition , Vol. 1 (Oxford Univ. Press, 2022).

Cheney, D. L. & Seyfarth, R. M. How Monkeys See the World: Inside the Mind of Another Species (Univ. Chicago Press, 1990).

Herrmann, E., Call, J., Hernández-Lloreda, M. V., Hare, B. & Tomasello, M. Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317 , 1360–1366 (2007).

Tomasello, M. & Herrmann, E. Ape and human cognition: what’s the difference? Curr. Dir. Psychol. Sci. 19 , 3–8 (2010).

Fischer, J. Monkeytalk: Inside the Worlds and Minds of Primates (Univ. Chicago Press, 2017).

Krupenye, C. & Call, J. Theory of mind in animals: current and future directions. Wiley Interdiscip. Rev. Cogn. Sci. 10 , e1503 (2019).

Shimizu, T. Why can birds be so smart? Background, significance, and implications of the revised view of the avian brain. Comparat. Cogn. Behav. Rev. 4 , 103–115 (2009).

Güntürkün, O. & Bugnyar, T. Cognition without cortex. Trends Cogn. Sci. 20 , 291–303 (2016).

Hart, B. L., Hart, L. A. & Pinter-Wollman, N. Large brains and cognition: where do elephants fit in? Neurosci. Biobehav. Rev. 32 , 86–98 (2008).

Godfrey-Smith, P. Other Minds: The Octopus and the Evolution of Intelligent Life (William Collins, 2016).

Schnell, A. K., Amodio, P., Boeckle, M. & Clayton, N. S. How intelligent is a cephalopod? Lessons from comparative cognition. Biol. Rev. 96 , 162–178 (2021).

Gallistel, C. R. Prelinguistic thought. Lang. Learn. Dev. 7 , 253–262 (2011).

Fitch, W. T. Animal cognition and the evolution of human language: why we cannot focus solely on communication. Philos. Trans. R. Soc. B 375 , 20190046 (2020).

Yamada, J. E. & Marshall, J. C. Laura: A Case Study for the Modularity of Language (MIT Press, 1990).

Rondal, J. A. Exceptional Language Development in Down Syndrome (Cambridge Univ. Press, 1995).

Bellugi, U., Lichtenberger, L., Jones, W., Lai, Z. & St George, M. The neurocognitive profile of Williams syndrome: a complex pattern of strengths and weaknesses. J. Cogn. Neurosci. 12 , 7–29 (2000).

Little, B. et al. Language in schizophrenia and aphasia: the relationship with non-verbal cognition and thought disorder. Cogn. Neuropsychiatry 24 , 389–405 (2019).

Mahowald, K. et al. Dissociating language and thought in large language models. Trends Cogn. Sci. 28 , 517–540(2024).

Chomsky, N., Belleti, A. & Rizzi, L. in On Nature and Language (eds Belleti, A. & Rizzi, L.) 92–161 (Cambridge Univ. Press, 2002).

Schwartz, J. L., Boë, L. J., Vallée, N. & Abry, C. The dispersion–focalization theory of vowel systems. J. Phonetics 25 , 255–286 (1997).

Diehl, R. L. Acoustic and auditory phonetics: the adaptive design of speech sound systems. Philos. Trans. R. Soc. B 363 , 965–978 (2008).

Everett, C., Blasi, D. E. & Roberts, S. G. Climate, vocal folds, and tonal languages: Connecting the physiological and geographic dots. Proc. Natl Acad. Sci. USA 112 , 1322–1327 (2015).

Blasi, D. E. et al. Human sound systems are shaped by post-Neolithic changes in bite configuration. Science 363 , eaav3218 (2019).

Dautriche, I., Mahowald, K., Gibson, E., Christophe, A. & Piantadosi, S. T. Words cluster phonetically beyond phonotactic regularities. Cognition 163 , 128–145 (2017).

Piantadosi, S. T., Tily, H. & Gibson, E. Word lengths are optimized for efficient communication. Proc. Natl Acad. Sci. USA 108 , 3526–3529 (2011).

Levelt, W. J. Speaking: From Intention to Articulation (MIT Press, 1993).

Kemp, C. & Regier, T. Kinship categories across languages reflect general communicative principles. Science 336 , 1049–1054 (2012). This study provides a computational demonstration that the kinship systems across world’s languages trade off between simplicity and informativeness in a near-optimal way, and argue that these principles also characterize other category systems .

Gibson, E. et al. Color naming across languages reflects color use. Proc. Natl Acad. Sci. USA 114 , 10785–10790 (2017).

Zaslavsky, N., Kemp, C., Regier, T. & Tishby, N. Efficient compression in color naming and its evolution. Proc. Natl Acad. Sci. USA 115 , 7937–7942 (2018).

Kemp, C., Gaby, A. & Regier, T. Season naming and the local environment. Proc. 41st Annual Meeting of the Cognitive Science Society 539–545 (2019).

Xu, Y., Liu, E. & Regier, T. Numeral systems across languages support efficient communication: From approximate numerosity to recursion. Open Mind 4 , 57–70 (2020).

Denić, M., Steinert-Threlkeld, S. & Szymanik, J. Complexity/informativeness trade-off in the domain of indefinite pronouns. Semant. Linguist. Theor. 30 , 166–184 (2021).

Mollica, F. et al. The forms and meanings of grammatical markers support efficient communication. Proc. Natl Acad. Sci. USA 118 , e2025993118 (2021).

van de Pol, I., Lodder, P., van Maanen, L., Steinert-Threlkeld, S. & Szymanik, J. Quantifiers satisfying semantic universals have shorter minimal description length. Cognition 232 , 105150 (2023).

Clark, H. H. in Context in Language Learning and Language Understanding (eds Malmkj’r, K. & Williams, J.) 63–87) (Cambridge Univ. Press, 1998).

Winter, B., Perlman, M. & Majid, A. Vision dominates in perceptual language: English sensory vocabulary is optimized for usage. Cognition 179 , 213–220 (2018).

von Humboldt, W. Uber die Verschiedenheit des Menschlichen Sprachbaues (1836).

Hurford, J. R. Linguistic Evolution Through Language Acquisition: Formal and Computational Models (ed. Briscoe, E.) 301–344 (Cambridge Univ. Press, 2002).

Smith, K., Brighton, H. & Kirby, S. Complex systems in language evolution: the cultural emergence of compositional structure. Adv. Complex Syst. 6 , 537–558 (2003).

Piantadosi, S. T. & Fedorenko, E. Infinitely productive language can arise from chance under communicative pressure. J. Lang. Evol. 2 , 141–147 (2017).

Gibson, E. Linguistic complexity: locality of syntactic dependencies. Cognition 68 , 1–76 (1998).

Lewis, R. L., Vasishth, S. & Van Dyke, J. A. Computational principles of working memory in sentence comprehension. Trends Cogn. Sci. 10 , 447–454 (2006).

Liu, H. Dependency distance as a metric of language comprehension difficulty. J. Cogn. Sci. 9 , 151–191 (2008).

ADS   CAS   Google Scholar  

Futrell, R., Mahowald, K. & Gibson, E. Large-scale evidence of dependency length minimization in 37 languages. Proc. Natl Acad. Sci. USA 112 , 10336–10341 (2015). This investigation of syntactic dependency lengths across 37 diverse languages suggests that dependencies are predominantly local cross-linguistically, presumably because non-local dependencies are cognitively costly in both production and comprehension .

Dryer, M. S. The Greenbergian word order correlations. Language 68 , 81–138 (1992).

Hahn, M., Jurafsky, D. & Futrell, R. Universals of word order reflect optimization of grammars for efficient communication. Proc. Natl Acad. Sci. USA 117 , 2347–2353 (2020).

Goldin-Meadow, S., Wing, C. S., Özyürek, A. & Mylander, C. The natural order of events: how speakers of different languages represent events nonverbally. Proc. Natl Acad. Sci. USA 105 , 9163–9168 (2008).

Senghas, A., Kita, S. & Ozyürek, A. Children creating core properties of language: evidence from an emerging sign language in Nicaragua. Science 305 , 1779–1782 (2004).

Sandler, W., Meir, I., Padden, C. & Aronoff, M. The emergence of grammar: systematic structure in a new language. Proc. Natl Acad. Sci. USA 102 , 2661–2665 (2005).

Gibson, E. et al. A noisy-channel account of crosslinguistic word-order variation. Psychol. Sci. 24 , 1079–1088 (2013).

Levy, R. A noisy-channel model of human sentence comprehension under uncertain input. In Proc. Conference on Empirical Methods in Natural Language Processing 234–243 (2008).

Gibson, E., Bergen, L. & Piantadosi, S. T. Rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proc. Natl Acad. Sci. USA 110 , 8051–8056 (2013). This behavioural investigation demonstrates that language comprehension is robust to noise: in the presence of corrupt linguistic input, listeners and readers rely on a combination of prior expectations about messages that are likely to be communicated and knowledge of how linguistic signals can get corrupted by noise .

Futrell, R., Levy, R. P. & Gibson, E. Dependency locality as an explanatory principle for word order. Language 96 , 371–412 (2020).

Hahn, M. & Xu, Y. Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality. Proc. Natl Acad. Sci. USA 119 , e2122604119 (2022).

Hahn, M., Futrell, R., Levy, R. & Gibson, E. A resource-rational model of human processing of recursive linguistic structure. Proc. Natl Acad. Sci. USA 119 , e2122602119 (2022).

Piantadosi, S. T., Tily, H. & Gibson, E. The communicative function of ambiguity in language. Cognition 122 , 280–291 (2012).

Quijada, J. A grammar of the Ithkuil language—introduction. ithkuil.net https://ithkuil.net/00_intro.html (accessed 27 February 2022).

Srinivasan, M. & Rabagliati, H. The implications of polysemy for theories of word learning. Child Dev. Perspect. 15 , 148–153 (2021).

Bizzi, E. Motor control revisited: a novel view. Curr. Trends Neurol. 10 , 75–80 (2016).

Darwin, C. On the Origin of Species–A Facsimile of the First Edition (Harvard Univ. Press, 1964).

Herculano-Houzel, S. The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. Proc. Natl Acad. Sci. USA 109 , 10661–10668 (2012).

White, L. T. The historical roots of our ecologic crisis. Science 155 , 1203–1207 (1967).

Article   ADS   PubMed   Google Scholar  

King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188 , 107–116 (1975).

Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437 , 69–87 (2005).

Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17 , 648–665 (2013). This review presents the evidence for the disproportionate expansion of the association cortex relative to other brain areas in humans .

Duncan, J., Assem, M. & Shashidhara, S. Integrated intelligence from distributed brain activity. Trends Cogn. Sci. 24 , 838–852 (2020).

Saxe, R. & Kanwisher, N. People thinking about thinking people: the role of the temporo-parietal junction in “theory of mind”. NeuroImage 19 , 1835–1842 (2003).

Buckner, R. L. & DiNicola, L. M. The brain’s default network: updated anatomy, physiology and evolving insights. Nat. Rev. Neurosci. 20 , 593–608 (2019).

Deen, B. & Freiwald, W. A. Parallel systems for social and spatial reasoning within the cortical apex. Preprint at bioRxiv https://doi.org/10.1101/2021.09.23.461550 (2021).

Mitchell, D. J. et al. A putative multiple-demand system in the macaque brain. J. Neurosci. 36 , 8574–8585 (2016).

Cantlon, J. & Piantadosi, S. Uniquely human intelligence arose from expanded information capacity. Nat. Rev. Psychol. 3 , 275–293 (2024).

Tomasello, M. The Cultural Origins of Human Cognition (Harvard Univ. Press, 2009).

Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: Why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108 , 10918–10925 (2011).

Henrich, J. The Secret of Our Success: How Culture is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (Princeton Univ. Press, 2016).

Heyes, C. Cognitive Gadgets (Harvard Univ. Press, 2018).

Gumperz, J. J. & Levinson, S. C. (eds). Rethinking Linguistic Relativity (Cambridge Univ. Press, 1996).

Piaget, J. Language and Thought of the Child: Selected Works , Vol. 5 (Routledge, 2005).

Gleitman, L. R. & Papafragou, A. in Cambridge Handbook of Thinking and Reasoning (eds Holyoak, K. & Morrison, R.) 2nd edn (Oxford Univ. Press, 2016).

Fedorenko, E. & Varley, R. Language and thought are not the same thing: evidence from neuroimaging and neurological patients. Ann. NY Acad. Sci. 1369 , 132–153 (2016).

Gentner, D. Language as cognitive tool kit: How language supports relational thought. Am. Psychol. 71 , 650 (2016).

Frank, M. C., Everett, D. L., Fedorenko, E. & Gibson, E. Number as a cognitive technology: Evidence from Pirahã language and cognition. Cognition 108 , 819–824 (2008).

Wernicke, C. The aphasic symptom-complex: a psychological study on an anatomical basis. Arch. Neurol. 22 , 280–282 (1869).

Lichteim, L. On aphasia. Brain 7 , 433–484 (1885).

Poeppel, D., Emmorey, K., Hickok, G. & Pylkkänen, L. Towards a new neurobiology of language. J. Neurosci. 32 , 14125–14131 (2012).

Tremblay, P. & Dick, A. S. Broca and Wernicke are dead, or moving past the classic model of language neurobiology. Brain Lang. 162 , 60–71 (2016).

Hillis, A. E. et al. Re‐examining the brain regions crucial for orchestrating speech articulation. Brain 127 , 1479–1487 (2004).

Flinker, A. et al. Redefining the role of Broca’s area in speech. Proc. Natl Acad. Sci. USA 112 , 2871–2875 (2015).

Long, M. A. et al. Functional segregation of cortical regions underlying speech timing and articulation. Neuron 89 , 1187–1193 (2016).

Guenther, F. H. Neural Control of Speech (MIT Press, 2016).

Basilakos, A., Smith, K. G., Fillmore, P., Fridriksson, J. & Fedorenko, E. Functional characterization of the human speech articulation network. Cereb. Cortex 28 , 1816–1830 (2018).

Obleser, J., Zimmermann, J., Van Meter, J. & Rauschecker, J. P. Multiple stages of auditory speech perception reflected in event-related fMRI. Cereb. Cortex 17 , 2251–2257 (2007).

Mesgarani, N., Cheung, C., Johnson, K. & Chang, E. F. Phonetic feature encoding in human superior temporal gyrus. Science 343 , 1006–1010 (2014).

Norman-Haignere, S., Kanwisher, N. G. & McDermott, J. H. Distinct cortical pathways for music and speech revealed by hypothesis-free voxel decomposition. Neuron 88 , 1281–1296 (2015).

Overath, T., McDermott, J., Zarate, J. & Poeppel, D. The cortical analysis of speech-specific temporal structure revealed by responses to sound quilts. Nat. Neurosci. 18 , 903–911 (2015).

Norman-Haignere, S. V. et al. A neural population selective for song in human auditory cortex. Curr. Biol. 32 , 1470–1484.e12 (2022).

Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8 , 393–402 (2007).

Friederici, A. D. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16 , 262–268 (2012).

Wilson, S. M. et al. Recovery from aphasia in the first year after stroke. Brain 146 , 1021–1039 (2023).

Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1 , 9 (2019).

Jain, S. & Huth, A. Incorporating context into language encoding models for fMRI. in Proc. 32nd International Conf. Neural Information Processing Systems (eds Bengio, S. et al.) (Curran Associates, 2018).

Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118 , e2105646118 (2021).

Caucheteux, C. & King, J. R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5 , 134 (2022).

Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25 , 369–380 (2022).

Tuckute, T., Kanwisher, N. & Fedorenko, E. Language in brains, minds, and machines. Annu. Rev. Neurosci. https://doi.org/10.1146/annurev-neuro-120623-101142 (2024).

Paulk, A. C. et al. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat. Neurosci. 25 , 252–263 (2022).

Leonard, M. K. et al. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 626 , 593–602 (2024).

Fodor, J. A. The Language of Thought (Crowell, 1975).

Fodor, J. A. & Pylyshyn, Z. W. Connectionism and cognitive architecture: a critical analysis. Cognition 28 , 3–71 (1988).

Rule, J. S., Tenenbaum, J. B. & Piantadosi, S. T. The child as hacker. Trends Cogn. Sci. 24 , 900–915 (2020).

Quilty-Dunn, J., Porot, N. & Mandelbaum, E. The best game in town: the reemergence of the language-of-thought hypothesis across the cognitive sciences. Behav. Brain Sci. 46 , e261 (2023).

Rumelhart, D. E., McClelland, J. L. & PDP Research Group. Parallel Distributed Processing, Vol. 1: Explorations in the Microstructure of Cognition: Foundations (MIT Press, 1986).

Smolensky, P. & Legendre, G. The Harmonic Mind: From Neural Computation to Optimality–Theoretic Grammar Vol. 1: Cognitive Architecture (MIT Press, 2006).

Frankland, S. M. & Greene, J. D. Concepts and compositionality: in search of the brain’s language of thought. Annu. Rev. Psychol. 71 , 273–303 (2020).

Lake, B. M. & Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623 , 115–121 (2023).

Dehaene-Lambertz, G., Dehaene, S. & Hertz-Pannier, L. Functional neuroimaging of speech perception in infants. Science 298 , 2013–2015 (2002).

Pena, M. et al. Sounds and silence: an optical topography study of language recognition at birth. Proc. Natl Acad. Sci. USA 100 , 11702–11705 (2003).

Cristia, A., Minagawa, Y. & Dupoux, E. Responses to vocalizations and auditory controls in the human newborn brain. PLoS ONE 9 , e115162 (2014).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

The authors thank A. Ivanova, R. Jackendoff, N. Kanwisher, K. Mahowald, R. Seyfarth, C. Shain and N. Zaslavsky for helpful comments on earlier drafts of the manuscript; N. Caselli, M. Coppola, A. Hillis, L. Menn, R. Varley and S. Wilson for comments on specific sections; C. Casto, T. Regev, F. Mollica and R. Futrell for help with the figures; and S. Swords, N. Jhingan, H. S. Kim and A. Sathe for help with references. E.F. was supported by NIH awards DC016607 and DC016950 from NIDCD, NS121471 from NINDS, and from funds from MIT’s McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Simons Center for the Social Brain, and Quest for Intelligence.

Author information

Authors and affiliations.

Massachusetts Institute of Technology, Cambridge, MA, USA

Evelina Fedorenko & Edward A. F. Gibson

Speech and Hearing in Bioscience and Technology Program at Harvard University, Boston, MA, USA

Evelina Fedorenko

University of California, Berkeley, Berkeley, CA, USA

Steven T. Piantadosi

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed equally to conceiving, writing and revising this piece.

Corresponding author

Correspondence to Evelina Fedorenko .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Angelika Kratzer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Fedorenko, E., Piantadosi, S.T. & Gibson, E.A.F. Language is primarily a tool for communication rather than thought. Nature 630 , 575–586 (2024). https://doi.org/10.1038/s41586-024-07522-w

Download citation

Received : 15 February 2023

Accepted : 03 May 2024

Published : 19 June 2024

Issue Date : 20 June 2024

DOI : https://doi.org/10.1038/s41586-024-07522-w

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.