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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.  

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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 ...

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!

Very interesting. Thank you. Please I need a PhD topic in climate change in relation to health.

Your posts have provided a clear, easy to understand, motivating literature, mainly when these topics tend to be considered “boring” in some careers.

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When has literacy become a widespread skill.

Literacy is a foundational skill. Children need to learn to read so that they can read to learn. When we fail to teach this foundational skill, people have fewer opportunities to lead the rich and interesting lives that a good education offers.

The historical data shows that only a very small share of the population, a tiny elite, was able to read and write. Over the course of the last few generations, literacy levels increased, but it remains an important challenge for our time to provide this foundational skill to all.

At Our World in Data, we investigated the strengths and shortcomings of the available data on literacy. Based on this work, our team brought together the long-run data shown in the chart by combining several different sources, including the World Bank, the CIA Factbook, and a range of research publications.

Share of the population with access to electricity World Bank

Where do people lack access to even the most basic electricity supply.

Light at night makes it possible to get together after sunset; mobile phones allow us to stay in touch with those far away; the refrigeration of food reduces food waste; and household appliances free up time from household chores. Access to electricity improves people’s living conditions in many ways.

The World Bank data on the world map captures whether people have access to the most basic electricity supply — just enough to provide basic lighting and charge a phone or power a radio for 4 hours per day.

It shows that, especially in several African countries, a large share of the population lacks the benefits that basic electricity offers. No radio and no light at night.

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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

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Ten Research Challenge Areas in Data Science

Although data science builds on knowledge from computer science, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: challenging scientific questions and pressing questions of societal importance.

Is data science a discipline?

Data science is a field of study: one can get a degree in data science, get a job as a data scientist, and get funded to do data science research.  But is data science a discipline, or will it evolve to be one, distinct from other disciplines?  Here are a few meta-questions about data science as a discipline.

• What is/are the driving deep question(s) of data science?   Each scientific discipline (usually) has one or more “deep” questions that drive its research agenda: What is the origin of the universe (astrophysics)?  What is the origin of life (biology)?  What is computable (computer science)?  Does data science inherit its deep questions from all its constituency disciplines or does it have its own unique ones?
• What is the role of the domain in the field of data science?   People (including this author) (Wing, J.M., Janeia, V.P., Kloefkorn, T., & Erickson, L.C. (2018)) have argued that data science is unique in that it is not just about methods, but about the use of those methods in the context of a domain—the domain of the data being collected and analyzed; the domain for which a question to be answered comes from collecting and analyzing the data.  Is the inclusion of a domain inherent in defining the field of data science?  If so, is the way it is included unique to data science?
• What makes data science data science?   Is there a problem unique to data science that one can convincingly argue would not be addressed or asked by any of its constituent disciplines, e.g., computer science and statistics?

Ten research areas

While answering the above meta-questions is still under lively debate, including within the pages of this  journal, we can ask an easier question, one that also underlies any field of study: What are the research challenge areas that drive the study of data science?  Here is a list of ten.  They are not in any priority order, and some of them are related to each other.  They are phrased as challenge areas, not challenge questions.  They are not necessarily the “top ten” but they are a good ten to start the community discussing what a broad research agenda for data science might look like. 1

• Scientific understanding of learning, especially deep learning algorithms.    As much as we admire the astonishing successes of deep learning, we still lack a scientific understanding of why deep learning works so well.  We do not understand the mathematical properties of deep learning models.  We do not know how to explain why a deep learning model produces one result and not another.  We do not understand how robust or fragile they are to perturbations to input data distributions.  We do not understand how to verify that deep learning will perform the intended task well on new input data.  Deep learning is an example of where experimentation in a field is far ahead of any kind of theoretical understanding.
• Causal reasoning.   Machine learning is a powerful tool to find patterns and examine correlations, particularly in large data sets. While the adoption of machine learning has opened many fruitful areas of research in economics, social science, and medicine, these fields require methods that move beyond correlational analyses and can tackle causal questions. A rich and growing area of current study is revisiting causal inference in the presence of large amounts of data.  Economists are already revisiting causal reasoning by devising new methods at the intersection of economics and machine learning that make causal inference estimation more efficient and flexible (Athey, 2016), (Taddy, 2019).  Data scientists are just beginning to explore multiple causal inference, not just to overcome some of the strong assumptions of univariate causal inference, but because most real-world observations are due to multiple factors that interact with each other (Wang & Blei, 2018).
• Precious data.    Data can be precious for one of three reasons: the dataset is expensive to collect; the dataset contains a rare event (low signal-to-noise ratio );  or the dataset is artisanal—small and task-specific.   A good example of expensive data comes from large, one-of, expensive scientific instruments, e.g., the Large Synoptic Survey Telescope, the Large Hadron Collider, the IceCube Neutrino Detector at the South Pole.  A good example of rare event data is data from sensors on physical infrastructure, such as bridges and tunnels; sensors produce a lot of raw data, but the disastrous event they are used to predict is (thankfully) rare.   Rare data can also be expensive to collect.  A good example of artisanal data is the tens of millions of court judgments that China has released online to the public since 2014 (Liebman, Roberts, Stern, & Wang, 2017) or the 2+ million US government declassified documents collected by Columbia’s  History Lab  (Connelly, Madigan, Jervis, Spirling, & Hicks, 2019).   For each of these different kinds of precious data, we need new data science methods and algorithms, taking into consideration the domain and intended uses of the data.
• Multiple, heterogeneous data sources.   For some problems, we can collect lots of data from different data sources to improve our models.  For example, to predict the effectiveness of a specific cancer treatment for a human, we might build a model based on 2-D cell lines from mice, more expensive 3-D cell lines from mice, and the costly DNA sequence of the cancer cells extracted from the human. State-of-the-art data science methods cannot as yet handle combining multiple, heterogeneous sources of data to build a single, accurate model.  Since many of these data sources might be precious data, this challenge is related to the third challenge.  Focused research in combining multiple sources of data will provide extraordinary impact.
• Inferring from noisy and/or incomplete data.   The real world is messy and we often do not have complete information about every data point.  Yet, data scientists want to build models from such data to do prediction and inference.  A great example of a novel formulation of this problem is the planned use of differential privacy for Census 2020 data (Garfinkel, 2019), where noise is deliberately added to a query result, to maintain the privacy of individuals participating in the census. Handling “deliberate” noise is particularly important for researchers working with small geographic areas such as census blocks, since the added noise can make the data uninformative at those levels of aggregation. How then can social scientists, who for decades have been drawing inferences from census data, make inferences on this “noisy” data and how do they combine their past inferences with these new ones? Machine learning’s ability to better separate noise from signal can improve the efficiency and accuracy of those inferences.
• Trustworthy AI.   We have seen rapid deployment of systems using artificial intelligence (AI) and machine learning in critical domains such as autonomous vehicles, criminal justice, healthcare, hiring, housing, human resource management, law enforcement, and public safety, where decisions taken by AI agents directly impact human lives. Consequently, there is an increasing concern if these decisions can be trusted to be correct, reliable, robust, safe, secure, and fair, especially under adversarial attacks. One approach to building trust is through providing explanations of the outcomes of a machine learned model.  If we can interpret the outcome in a meaningful way, then the end user can better trust the model.  Another approach is through formal methods, where one strives to prove once and for all a model satisfies a certain property.  New trust properties yield new tradeoffs for machine learned models, e.g., privacy versus accuracy; robustness versus efficiency. There are actually multiple audiences for trustworthy models: the model developer, the model user, and the model customer.  Ultimately, for widespread adoption of the technology, it is the public who must trust these automated decision systems.
• Computing systems for data-intensive applications.    Traditional designs of computing systems have focused on computational speed and power: the more cycles, the faster the application can run.  Today, the primary focus of applications, especially in the sciences (e.g., astronomy, biology, climate science, materials science), is data.  Also, novel special-purpose processors, e.g., GPUs, FPGAs, TPUs, are now commonly found in large data centers. Even with all these data and all this fast and flexible computational power, it can still take weeks to build accurate predictive models; however, applications, whether from science or industry, want  real-time  predictions.  Also, data-hungry and compute-hungry algorithms, e.g., deep learning, are energy hogs (Strubell, Ganesh, & McCallum, 2019).   We should consider not only space and time, but also energy consumption, in our performance metrics.  In short, we need to rethink computer systems design from first principles, with data (not compute) the focus.  New computing systems designs need to consider: heterogeneous processing; efficient layout of massive amounts of data for fast access; the target domain, application, or even task; and energy efficiency.
• Automating front-end stages of the data life cycle.   While the excitement in data science is due largely to the successes of machine learning, and more specifically deep learning, before we get to use machine learning methods, we need to prepare the data for analysis.  The early stages in the data life cycle (Wing, 2019) are still labor intensive and tedious.  Data scientists, drawing on both computational and statistical methods, need to devise automated methods that address data cleaning and data wrangling, without losing other desired properties, e.g., accuracy, precision, and robustness, of the end model.One example of emerging work in this area is the Data Analysis Baseline Library (Mueller, 2019), which provides a framework to simplify and automate data cleaning, visualization, model building, and model interpretation.  The Snorkel project addresses the tedious task of data labeling (Ratner et al., 2018).
• Privacy.   Today, the more data we have, the better the model we can build.  One way to get more data is to share data, e.g., multiple parties pool their individual datasets to build collectively a better model than any one party can build.  However, in many cases, due to regulation or privacy concerns, we need to preserve the confidentiality of each party’s dataset.  An example of this scenario is in building a model to predict whether someone has a disease or not. If multiple hospitals could share their patient records, we could build a better predictive model; but due to Health Insurance Portability and Accountability Act (HIPAA) privacy regulations, hospitals cannot share these records. We are only now exploring practical and scalable ways, using cryptographic and statistical methods, for multiple parties to share data and/or share models to preserve the privacy of each party’s dataset.  Industry and government are exploring and exploiting methods and concepts, such as secure multi-party computation, homomorphic encryption, zero-knowledge proofs, and differential privacy, as part of a point solution to a point problem.
• Ethics.   Data science raises new ethical issues. They can be framed along three axes: (1) the ethics of data: how data are generated, recorded, and shared; (2) the ethics of algorithms: how artificial intelligence, machine learning, and robots interpret data; and (3) the ethics of practices: devising responsible innovation and professional codes to guide this emerging science (Floridi & Taddeo, 2016) and for defining Institutional Review Board (IRB) criteria and processes specific for data (Wing, Janeia, Kloefkorn, & Erickson 2018). Example ethical questions include how to detect and eliminate racial, gender, socio-economic, or other biases in machine learning models.

Closing remarks

As many universities and colleges are creating new data science schools, institutes, centers, etc. (Wing, Janeia, Kloefkorn, & Erickson 2018), it is worth reflecting on data science as a field.  Will data science as an area of research and education evolve into being its own discipline or be a field that cuts across all other disciplines?  One could argue that computer science, mathematics, and statistics share this commonality: they are each their own discipline, but they each can be applied to (almost) every other discipline. What will data science be in 10 or 50 years?

Acknowledgements

I would like to thank Cliff Stein, Gerad Torats-Espinosa, Max Topaz, and Richard Witten for their feedback on earlier renditions of this article.  Many thanks to all Columbia Data Science faculty who have helped me formulate and discuss these ten (and other) challenges during our Fall 2019 retreat.

Athey, S. (2016). “Susan Athey on how economists can use machine learning to improve policy,”  Retrieved from  https://siepr.stanford.edu/news/susan-athey-how-economists-can-use-machine-learning-improve-policy

Berger, J., He, X., Madigan, C., Murphy, S., Yu, B., & Wellner, J. (2019), Statistics at a Crossroad: Who is for the Challenge? NSF workshop report.  Retrieved from  https://hub.ki/groups/statscrossroad

Connelly, M., Madigan, D., Jervis, R., Spirling, A., & Hicks, R. (2019). The History Lab.  Retrieved from   http://history-lab.org/

Floridi , L. &  Taddeo , M. (2016). What is Data Ethics?  Philosophical Transactions of the Royal Society A , vol. 374, issue 2083, December 2016.

Garfinkel, S. (2019). Deploying Differential Privacy for the 2020 Census of Population and Housing. Privacy Enhancing Technologies Symposium, Stockholm, Sweden.  Retrieved from  http://simson.net/ref/2019/2019-07-16%20Deploying%20Differential%20Privacy%20for%20the%202020%20Census.pdf

Liebman, B.L., Roberts, M., Stern, R.E., & Wang, A. (2017).  Mass Digitization of Chinese Court Decisions: How to Use Text as Data in the Field of Chinese Law. UC  San Diego School of Global Policy and Strategy, 21 st  Century China Center Research Paper No. 2017-01; Columbia Public Law Research Paper No. 14-551. Retrieved from  https://scholarship.law.columbia.edu/faculty_scholarship/2039

Mueller, A. (2019). Data Analysis Baseline Library. Retrieved from  https://libraries.io/github/amueller/dabl

Ratner, A., Bach, S., Ehrenberg, H., Fries, J., Wu, S, & Ré, C. (2018).  Snorkel: Rapid Training Data Creation with Weak Supervision . Proceedings of the 44 th  International Conference on Very Large Data Bases.

Strubell E., Ganesh, A., & McCallum, A. (2019),”Energy and Policy Considerations for Deep Learning in NLP.  Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL).

Taddy, M. (2019).   Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions , Mc-Graw Hill.

Wang, Y. & Blei, D.M. (2018). The Blessings of Multiple Causes, Retrieved from  https://arxiv.org/abs/1805.06826

Wing, J.M. (2019), The Data Life Cycle,  Harvard Data Science Review , vol. 1, no. 1. 

Wing, J.M., Janeia, V.P., Kloefkorn, T., & Erickson, L.C. (2018). Data Science Leadership Summit, Workshop Report, National Science Foundation.  Retrieved from  https://dl.acm.org/citation.cfm?id=3293458

J.M. Wing, “ Ten Research Challenge Areas in Data Science ,” Voices, Data Science Institute, Columbia University, January 2, 2020.  arXiv:2002.05658 .

Jeannette M. Wing is Avanessians Director of the Data Science Institute and professor of computer science at Columbia University.

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• Narrowing a Topic Idea
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• Academic Writing Style
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• Scholarly vs. Popular Publications
• Qualitative Methods
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• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
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• Further Readings
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• 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.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on 8 November 2022 by Shona McCombes and Tegan George.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism, run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organisation. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organisation faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organisation focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organisation requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.

Research objectives describe what you intend your research project to accomplish.

They summarise the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

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Common pitfalls in the research process.

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• Definition/Introduction

Conducting research from planning to publication can be a very rewarding process. However, multiple preventable setbacks can occur within each stage of research. While these inefficiencies are an inevitable part of the research process, understanding common pitfalls can limit those hindrances. Many issues can present themselves throughout the research process. It has been said about academics that “the politics are so harsh because the stakes are so low.” Beyond interpersonal and political / funding concerns, prospective authors may encounter some disenchantment with the publish or perish culture. With a metric of (any) publication, the motivation to contribute meaningfully to science can be overshadowed by a compulsive drive to publish. [1]  We believe in quality over quantity and highlight the importance of channeling creativity when pursuing scholarly work.

When considering embarking on a medical research project, one must begin with detailed planning. Do not underestimate the amount of time a project can take, often spanning years from conception to manuscript preparation. Will you conduct a retrospective chart review, a prospective study, or a true clinical trial with randomization and blinding? Will you systematically seek out and remove sources of bias from the study design and interpretation of results? Will you ensure the study is powered properly to justify conclusions? Will you eliminate or explain any conflicts of interest occurring among your author group? Will you fall victim to the temptation of frivolous subgroup analyses, or will you stick with the original plan? Will your study have a realistic chance at publication in a journal within your specialty, or perhaps another subfield? The study results may prove the null hypothesis, a ‘negative study,’ and therefore be difficult to publish. [2]  Additionally, the intervention you find beneficial may subsequently be proven unhelpful or even dangerous, leading to prudent medical reversal. [3]

These considerations and more necessitate meticulous planning and vigilant adherence to a sound protocol. Along the way, you will encounter obstacles, pitfalls, some of which are presented in this article. But remain persistent, and your efforts will be rewarded with publication and contribution to science. This review covers common pitfalls researchers encounter and suggested strategies to avoid them.

• Issues of Concern

There are five phases of research: planning phase, data collection/analysis phase, writing phase, journal submission phase, and rejections/revisions/acceptance phase.

Phase I Pitfalls: Planning a Study

The highest yield preempting of pitfalls in the research process occurs in the planning phase. This is when a researcher can set the stage for an optimal research process. Below are pitfalls that can occur during the planning phase.

Pitfall: Underestimating what committing to a research project requires

Conducting a research study and achieving publication sounds fulfilling, right?

Consider the many steps: conducting a literature search, writing an IRB proposal, planning and having research meetings, long and cumbersome data collection processes, working with statisticians or analyzing complex data, having unexpected research setbacks (e.g., subjects drop out, newly published papers on same topic, etc.), the possibility that after data collection you have no statistically (or clinically) significant findings, conducting an updated literature search, writing introduction, methods, results, and discussion sections of a paper, going through the many journal options to determine best fit while aiming for high impact factors, adhering to journal guidelines/fixing drafts, writing cover letters stating importance of the topic to respective journals, creating journal portal accounts, possibly being rejected numerous times, waiting months for journal decisions, working on numerous revisions and being informed by numerous individuals about all of the flaws in your writing and research.

Does it sound, maybe less fulfilling ?

Conducting a research project from inception to publication can be a rewarding experience. Research requires significant time. Setbacks are normal. To produce an important and sought-after research product, an individual must understand the magnitude of commitment required.

Pitfall: Choosing the wrong research pursuit/topic lacks precision

Consider an investigator interested in substance use research. The first challenge is the immense amount of research already published on this topic. Fortunately, there is still a massive amount of uncharted territory in substance use research.

It is important to understand what has been done and what is still undiscovered in your area of research. Do not simply study a topic because you find it interesting; passion is advantageous, but you should ensure that your study will contribute to some field/specialty or research in a significant way.

How does your research differ from what has been done?

How will it impact practice in a way that no previous study has?

Consider these questions when choosing a topic for research. Otherwise, you may struggle to get the work published. It can be demoralizing if you have already written your paper and realize that your paper is not going to get accepted by a reputable journal due to the presence of other papers already describing the same concepts you have.

As always, the first step is a thorough literature search.

Pitfall: Not considering research bias

A common theme noted in literature is that bias can, unfortunately, lead to failure to reproduce results, raising concerns regarding the integrity of science. [4]  Bias can be considered various (inadvertent) poor strategies related to data design, analysis, and results reporting that produce spurious results and papers that perhaps should not be published. [5]

While one cannot completely eliminate bias from the research process, researchers should take steps to understand research bias in study endeavors and determine how to minimize bias during the planning phase of the study.  

Pitfall: Not focusing on which variables to collect

Researchers often want to collect as much data as possible but should not build a list of variables that includes every single detail about subjects if the variables collected are unlikely to yield insight into the topic of research. The longer the data collection instrument, the higher likelihood of (human) errors (if manually data entry) and the longer duration of the data collection phase. Instead of taking time to build a database with many variables, consider cutting irrelevant variables and use that time to increase the sample size. Determine, based on your own clinical knowledge and published empirical works, which variables are most crucial. 

Pitfall: Worrying about the statistics after the data has been collected

A vital part of the research process is ensuring you have a rigorous statistical approach. Involve your statistician very early in the project, preferably in the planning stages. They will have insight into the types of variables to collect and help shape the research methods. Statistical power is an important concept to consider before data collection to avoid false-negative results (Zlowodzki et al., 2006). Furthermore, other concepts, such as covariates, need to be part of the planning phase. Do not wait until after the data collection phase to give data to the statistician who cannot transform the data you have into outputs you want.

Pitfall: Not setting defined author roles

It is important to define who will be declared authors at the beginning of the research process to avoid conflict. Do most people want to be an author? Sure. Does everybody do the work worthy of authorship? No. While placing general comments in a shared document's margin may make the paper slightly better, it probably should not qualify for authorship. Review authorship criteria to determine what constitutes authorship. Clear expectations can ensure that everyone is on the same page and that everyone feels the process is fair, especially for individuals who plan to invest significant time in the project. Clear expectations for each author should occur before any writing begins, including deadlines and specific contributions. [6] [7] [6]

Pitfall: Not considering limitations of work before the paper is written

Avoid this pitfall by reviewing recent manuscripts and reading the limitations sections of these papers. Many of these limitations sections will make notions about generalizability to other populations. Some will discuss low power. Even the best papers in the top journals have many limitations. The best way to avoid or mitigate your work's limitations is to consider them during the planning phase.

How can you set up your project to limit your limitations section?

What (types of) samples should you include in your study?

Were you originally thinking of retrospective design, but it could be prospective?

What steps can you utilize to control baseline characteristics between groups?

Consider all limitations and think about how you can control these before data collection.

Phase II Pitfalls: Data Collection and Analysis

After the planning has occurred, typically after institutional review board (IRB) approval, the data collection and analysis phase can transpire. The entire team should typically stay involved throughout these phases. Below are pitfalls to avoid.

Pitfall: Not being involved in the data collection phase

It is important to be involved with the data collection phase, even if you do not personally collect data. Train the individuals who collect data to ensure all are on the same page and provide periodic oversight to ensure accuracy and quality of the data over time. [8]  Do not assume the data collection phase is going smoothly – you may find yourself with a huge dataset riddled with inconsistencies or errors. Schedule periodic meetings to review data.

Pitfall: Not being involved with the statistical analysis phase

If you are not conducting the statistical analysis, do not assume that the person who is analyzing the data is 100% on the same page. Have meetings about the data, how to interpret the data, and the limitations of the data. Ask what other ways the data could be analyzed and how reviewers might negatively critique the data itself or the statistical methods.

The person conducting the analysis will not have the same familiarity with the topic. You are not going to be as familiar with the outputs. By understanding each other, you will a) have clearer, more robust methods and results in sections of the paper, b) limit critiques regarding the statistical approach/data outcomes, c) understand your research better for any presentations, discussion, or future work, and d) develop a positive collaboration for future work.

Phase III Pitfalls: The Writing Phase

The next phase is the writing phase. While this section covers pitfalls during the writing phase, for recommendations on conducting a literature search, writing, and publishing research, see StatPearls Evidence-Base Medicine Chapter: How to Write and Publish a Scientific Manuscript. [9]  Below are pitfalls that can occur during the writing phase. 

Pitfall: Poor or outdated references

When writing your paper, perform multiple literature searches to ensure all recent, salient references are covered—claims about recent similar work or research that frames your study if the references are outdated. Journals may even ask reviewers to comment on the presence or absence of up-to-date/suitable references. Conduct a literature search prior to data collection and stay on top of references throughout the research process as new papers become available.

Pitfall: No clearly defined purpose of the paper

Many aspects of manuscripts can get overlooked. Lack of a clear purpose statement can doom a paper to futility. Remind the readers of the goal of the project. You do not want consumers of your research to read the results section and forget what the goals/main outcomes are. The purpose statement should be located at the end of the introduction section.  

Pitfall: Unclear methods making research hard to reproduce

A common concern in science is the lack of transparency in methods for reproducibility. The methods section should allow a reader to understand exactly what was done and conduct the study. Consider examining the S treng T hening the R eporting of OB servational studies in E pidemiology (STROBE) checklist for the methods (as well as other paper sections) to ensure best reporting practices for reproducibility. [10]

Pitfall: The tables and narratives are the same

Reviewers prefer you not to state findings in narratives that are in tables. Tables focus readers on the most important results and are not redundant with the written content. Make call-outs to the table in the paper's narrative sections, but do not state information found in tables.  

Pitfall: Not reporting all data/outcomes

Some authors will state the main outcome of interest or have a statement such as “there were no other statistically significant findings between other groups.” Authors must report all outcomes and statistical analyses for reproducibility of the research. While this may be difficult to do with a broad approach, utilize tables and appendices to report all outcomes to show transparency and limit researcher bias.

Pitfall: Repeating results in discussion

Do not simply restate in the discussion what you already have in the results section. Utilize this section of the paper to link other references to your work and reflect on other empirical investigations' similarities or differences. Explain why your research provides an impactful contribution to the topic.  

Pitfall: Making conclusions that do not align with your work

Authors sometimes note in their conclusions how the work impacts a topic due to X reason when X may be too broad a claim and the work doesn’t really support or prove that notion. Researchers should align their conclusions to their own results and highlight the significance of their findings.

Pitfall: Thinking the title is not a big deal

A strong title will help with the impact/readership of your paper. Consider keeping a short title that provides the main takeaway. Papers with more concise titles and present the study conclusion result in a bigger impact/receive more citations. [11]

Pitfall: Completing the abstract last minute

Similar to the title, do not underestimate an abstract. Journal and conference reviewers (and the general audience) may only read your abstract. The abstract must have the key results and contributions of the study and be well-written.

Phase IV Pitfalls: Submitting to a Journal

After the paper has been written, it is time to choose the journal. This phase also has numerous pitfalls. Below are pitfalls that can occur during this phase.  

Pitfall: Choosing the wrong journal

Choosing the journal for your work can be overwhelming due to the number of options. Always look at the aims and scope of prospective journals. Look through the author guidelines to ensure that your manuscript adheres. This will save time. Review your reference list for any journals that appear more than once; if so, consider submitting to that journal. You do not want to submit your paper, wait two weeks, and then get a desk rejection because the editors state the paper is not aligned to the journal's aims and scope.

Additionally, researchers can aim too high and spend months (and numerous hours in journal submission portals) trying to publish a manuscript in a journal with a very large impact factor. Though admirable, if the research design and results lacking “gold standard” reporting, authors should consider a journal that is more likely to accept. Find a balance between the quality of your paper and the quality of the journal. Seek feedback from the other authors and/or senior colleagues who can provide honest feedback.

Pitfall: Poor cover letter on journal submission

Do not submit work with a flawed cover letter (errors or lack of clarity in how your work contributes to the body of literature). Spend time writing a detailed cover letter once, have it edited by someone else, and utilize that for all future projects. You can highlight the differences (e.g., the purpose of this work, our results showed) with each project. Use the cover letter to highlight the significance of the study while adhering to the disclosure guidelines (e.g., conflicts of interests, authors contributions, data releases, etc.), which will help the editorial board determine not only the suitability of the paper for the journal but also streamline the review process. [12]

Pitfall: Assuming that after the paper has been submitted to a journal, the work is done             

The paper has been submitted! You think you are finished…but, unfortunately, the publishing game may still be far from over. Researchers often do not recognize the amount of time going into the submission/rejection/revisions phases. Revisions can sometimes be total overhauls, more work than writing a whole new paper. Be prepared to continue working.

Phase V Pitfalls: The Rejections, Revisions, and Acceptance Phase

Finally, perhaps the most unpredictable phase, the rejections, revisions, and acceptance phase, has unique pitfalls and other obstacles.

Pitfall: Mourning rejections too long/ “sitting on” a rejected paper             

Did you get a desk to reject (i.e., the manuscript was not even sent for blind review)? That is unfortunate but common. You do not have time to sulk. Get that paper submitted somewhere else. The older the data, the less desirable your paper becomes. If the paper went in for a full review and was rejected, that may be even tougher than a desk reject because more time has elapsed. The good news is that (hopefully) you received feedback to incorporate in a revision. Do not spend too much time grieving rejections.

Pitfall: Not laying to rest rejected papers when it is indeed their time to go

Did you write a paper a couple of years ago, and you’ve submitted it to 20 different journals? The data is getting old. The topic wasn’t focused on. The sample size was small. Perhaps the project is not worth pursuing any longer. Do not give in to the sunk cost fallacy. If, however, you are proud of the work and stand by the paper, do not give up. If you believe after the numerous rejections that the topic/project is flawed, you can use this failure as a personal learning/growth opportunity. Do not repeat controllable mistakes on future projects.

Pitfall: Not addressing all of reviewer feedback

Did you get a revise and resubmit? Great news! The reviewers and editors will likely ask you to respond to each comment when you resubmit. Address all of the reviewer feedback. Take your time reading through the feedback, digest it, and re-read it. Carefully respond and decide how to revise your manuscript based on the feedback. Share the reviews and the duties of revision with coauthors. In your response to reviewers, stay professional and address each statement, even if you disagree with what is stated. If you do not respond to each statement, the reviewers often highlight the concern(s) again.

Pitfall: Thinking you know what the reviewers are going to say

Research reviewers are like a box of chocolates. You never know what you are going to get. You may be worried about a section of your paper/research approach, and the reviewers do not mention it at all in their review; instead, they criticize a section of your manuscript that you are most proud of.

In some reviews, you may get feedback like the following:

Reviewer #1

Please change lines 104-108 as I believe they are irrelevant to your study.

Reviewer #2

Please build on lines 104-108, as I believe they are the foundation of your study.

Sometimes, after multiple revisions, there are new concerns presented by the reviewers. This can be disheartening. Should some regulations restrict reviewers from bringing up new ideas/concerns during revision #7? Perhaps. Does any current rule prevent them from doing this? No.

During the review process, we must have faith that the reviewers are knowledgeable and provide fair, insightful, and constructive feedback. While the review process can be arbitrary or frustrating in some cases, peer review remains the gold standard in a scientific publication. Stay positive and persistent. Stay professional in responses to the reviewers. Remember that the review process can be very beneficial as it often leads to feedback that truly elevates your work and makes the product (and you) look better. [13]

Pitfall: Not rewarding yourself for a published paper

You did it! Celebrate your accomplishment. Reflect on the merit of your effort before you move on to other work or re-enter the cycle of IRBs, data coding, journal submissions, etc. Remember and appreciate how remarkable it is that you just contributed knowledge to the world.

• Clinical Significance

Many pitfalls can occur throughout the research process. Researchers should understand these pitfalls and utilize strategies to avoid them to produce high-quality, sought-after research results that are useful for basic science and clinical practice.

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Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Common Pitfalls In The Research Process. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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IJPDS International Journal of Population Data Science

Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.

Main article content.

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. 

This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. 

We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. 

Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

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FDA Issues Warning Letters to Two Chinese Firms Regarding Data Quality and Integrity Concerns, Violative Lab Practices

FDA News Release

Firms Provided Third-Party, Nonclinical Premarket Testing; Agency Review Ongoing

Today, the U.S. Food and Drug Administration issued warning letters to two Chinese nonclinical testing laboratories, citing both for laboratory oversight failures and animal care violations that raise concerns about the quality and integrity of data generated by the labs. Warning letters were issued to Mid-Link Testing Company Ltd . in Tianjin, China, and Sanitation & Environmental Technology Institute of Soochow University Ltd. in Suzhou, China. The firms provide third-party testing and validation data services to device manufacturers for use in their premarket device submissions to the FDA. 

The FDA continues to conduct a rigorous review of data generated from these test facilities, submitted in premarket submissions, and does not intend to authorize submissions where the data are necessary for the FDA to make a marketing authorization decision, as such data are found to be unreliable. The agency is evaluating any impact these findings have had on past submissions and will take action to address any public health risks as necessary.

The agency inspected the firms earlier this year and found pervasive failures with data management, quality assurance, staff training and oversight. The findings included the failure to accurately record and verify key research data, which brings into question the quality and integrity of safety data collected at the facilities. These failures could lead to the use of unreliable data in premarket device submissions. The warning letters also note violations related to test animals. One firm is cited for failing to provide adequate care for the animals, and both firms failed to provide adequate identification and recording of the animals used in the labs’ testing. 

“The medical device industry must be built and sustained on safety, effectiveness and quality,” said Owen Faris, Ph.D., acting director of the Office of Product Evaluation and Quality in the FDA’s Center for Devices and Radiological Health. “The FDA will take action to protect patients, consumers and the medical device supply chain from quality failures and violative practices. We strenuously remind industry of their responsibility and accountability for all data included in their submissions, which are required to comply with federal law.” 

Earlier this year, the agency alerted the medical device industry to third-party testing lab concerns with device submissions and stressed the need for firms to carefully review any data from testing that the firm itself did not perform. The FDA will continue to evaluate submissions and take action where appropriate, as some devices affected may be currently on the market. The FDA will continue to focus on testing data failures, including from third-party testing labs.

Nonclinical laboratory studies are experiments in which test articles are studied prospectively in test systems (such as animals, plants and microorganisms or subparts thereof) under laboratory conditions to determine their safety. While a device sponsor may use a third-party lab for nonclinical studies, doing so does not relieve the device sponsor of the responsibility to ensure the accuracy of data included in their regulatory submission.

The FDA has requested that the recipients of the FDA warning letters notify the agency of their corrective actions to be taken within 15 working days of receiving the letters.

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

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

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

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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East Tennessee State University’s Applied Social Research Lab will release a series of poll results from the sixth iteration of The Tennessee Poll starting the week of Sept. 20.

The poll surveyed 701 Tennesseans from July 10-17 of this year through telephone and text-to-web and has a margin of error of +/-3.7%. Results will be provided for topic areas for Tennesseans overall, as well as other key demographic characteristics.

The Tennessee Poll asked a series of questions related to health, education and quality of life for Tennesseans. The topics were wide-ranging and included questions on the current state of the nation and Tennessee, women’s reproductive health, legislation addressing access to guns, restriction and removal of classroom and library materials, election integrity, food insecurity and the role of colleges and universities in preparing today’s youth. Results of the poll will be released as follows:

• Biggest issues facing Tennessee and the USA – Sept. 20
• Legislation addressing access to guns – Oct. 4
• Women’s reproductive health – Oct. 11
• Restriction and removal of library and classroom materials – Oct. 18
• Election integrity – Oct. 25
• Food insecurity – Nov. 1
• The role of colleges and universities – Nov. 8

To sign up to directly receive these Tennessee Poll news releases, visit etsu.edu/tnpoll and sign up to be on the TN Poll mailing list.

About The Tennessee Poll

The Tennessee Poll is conducted by the Applied Social Research Lab (ASRL) in the Department of Sociology and Anthropology at East Tennessee State University. ASRL is directed by Dr. Kelly N. Foster, professor of sociology.

The Tennessee Poll is a public opinion poll funded by East Tennessee State University. The mission of The Tennessee Poll is to provide the citizens and governance of Tennessee with neutral, unbiased information on Tennesseans’ perceptions of issues that impact their health, education and quality of life.

Though the project has been internally funded to date, there exists the possibility of outside researchers or organizations being given the option to purchase space for questions on future polls. Should this occur, any and all funding sources will be noted in the methodology report for that particular poll.

The ASRL is a member of the Association of Academic Survey Research Organizations and adheres to the reporting requirements of the American Association for Public Opinion Research Transparency Initiative standards in research reporting.

For detailed information on The Tennessee Poll, including methodology and additional analysis, visit etsu.edu/tnpoll . 

Survey Methodology

The Tennessee Poll uses a combination of landline and cell phone numbers randomly selected from a list of Tennessee residents aged 18 and older. Braun Research Inc. handled the cell phone sample and conducted the telephone interviews. The interviews took place between 1 p.m. and 8 p.m., with interviewers attempting to reach each respondent up to five times. The study was conducted from July 10-17, 2024. The average interview lasted 17 minutes.

The final sample consists of 701 completed interviews: 197 via landline (28%), 162 via cell phone (23%), and 342 via TTW (49%). The TTW option was introduced as a new method for residents to participate in The Tennessee Poll. The final data are weighted by age, education, sex, income and race to adjust for differential response rates in order to assure that the data are as closely representative of the state’s actual adult population as possible. The margin of error for a sample of 701 is +/- 3.7 percentage points at the 95% confidence level for the entire sample. Any subpopulation analysis entails a greater margin of error. For detailed methodology, margin of error reports and additional analysis, visit etsu.edu/tnpoll .

East Tennessee State University was founded in 1911 with a singular mission: to improve the quality of life for people in the region and beyond. Through its world-class health sciences programs and interprofessional approach to health care education, ETSU is a highly respected leader in rural health research and practices. The university also boasts nationally ranked programs in the arts, technology, computing, and media studies. ETSU serves approximately 14,000 students each year and is ranked among the top 10 percent of colleges in the nation for students graduating with the least amount of debt.

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Could gut problems increase your risk of Parkinson's disease? New research points to yes

• kanaruhalter

Parkinson’s disease is a neurological disorder that attacks the nervous system. Researchers have long assumed that the disease starts in the brain, but a growing body of research indicates that, for some patients, it may actually start in the gut. 

A new study led by researchers at Beth Israel Deaconess Medical Center found that individuals with a history of upper gastrointestinal damage, like ulcers, were at a 76% greater risk of developing Parkinson’s than those without such history.

Dr. Trisha Pasricha, senior author of the study, joined GBH’s All Things Considered host Arun Rath to explain the link between Parkinson’s disease and gut health. What follows is a lightly edited transcript.

Arun Rath: The list of symptoms we traditionally associate with Parkinson’s — you know, we think of things like a tremor or stiffness of movement — [don’t seem] to have anything to do with the GI tract. I’m wondering, when did researchers first get a sense that there might be a connection between the GI tract and Parkinson’s?

Dr. Trisha Pasricha: Yeah, that’s a great question. The idea that Parkinson’s might originate in the gut is a hypothesis that’s at least two decades old.

But, you know, the interesting thing is that when you talk to patients with Parkinson’s disease, a lot of them [are] affected by tremors and difficulty walking. But when you ask them [about their gut health], they say, “You know, come to think of it, I have experienced constipation that started years before the motor symptoms started.” Or they might say, “Yeah, I started to get this low-level nausea and I don’t know why, but that’s been going on for years.”

As more and more people started to experience this, researchers like myself and others in the field started to say, “Well, why?” Why do these people tend to have symptoms like constipation, irritable bowel syndrome, nausea and trouble swallowing — all of these GI symptoms — years, if not decades, before these other more commonly associated symptoms like tremors started?

Rath: Working from there — going back from two decades to where you are with your research — where did that lead?

Pasricha: Yeah. So, about a little more than two decades ago, this hypothesis came out, which is known as Braak’s hypothesis , based on the researcher who first really described it in the literature. The idea is that there’s some trigger that happens outside the brain and could be in the gut that causes this misfolding of the protein alpha-synuclein, which we know plays a role in the death of dopaminergic neurons in the brain.

Actually, we hypothesized that some trigger [causes] that protein to misfold, potentially in the gut first. As that protein misfolds, it propagates up from the gut through the vagus nerve and into the brain, where it ultimately does cause those classic symptoms, like tremors, difficulty walking and rigidity.

Rath: Does that lead to a way of maybe understanding the onset of Parkinson’s earlier than we might typically?

Pasricha: That’s the hope. I mean, you know, most of these symptoms — the GI symptoms — often start much earlier than the motor symptoms. The whole idea with the gastroenterologists in this field, as I am, is to try to identify exactly, if we can, what those triggers are that happen in the gut.

That’s what led to the current study and whether any of the pathology that happens in the gut before it reaches the brain can be intervened upon early. Our study was based on this hypothesis that was described in the literature [and was] only really mentioned once in a paper from the 1960s, in which a physician noted [their] patients with Parkinson’s tend to have more duodenal and gastric ulcers than other patients, and he didn’t quite understand why. And then the medical literature goes silent for a few decades on this question.

But then, when I became a neuro-gastroenterologist — and I’ve been practicing now for several years — I, too, started to notice among my own Parkinson’s patients that these people tend to report not just gastric ulcers but erosions and gut troubles years before they got their diagnosis.

That’s what led us to do this study in a large cohort of patients where we really examine what happens to people who have injury to the mucosa, or the lining of their guts. Are those people actually at an increased risk of getting Parkinson’s disease? And indeed, that is what we discovered.

Rath: Parkinson’s — just stating the obvious — it’s a terrible disease. It’s progressive. There’s no cure for it. Is it possible that if we could understand those kinds of triggers you’re talking about, it could be possible at some point to stop Parkinson’s before it starts?

Pasricha: Well, that is the hope, and certainly, that’s what we want to achieve one day with some of this research. You’re right that we don’t have any medications that, at this point, can halt the progression in the way that we’d like to see.

But if, at least in a subset of patients, we can confirm that the disease has started somewhere in the gut we do hope that we can identify what those pathways are and then target some sort of treatment. We’re not quite there yet.

A lot of people, after [reading about] the study, have been asking, “Well, what I can do to reduce my risk of Parkinson’s if I had peptic ulcers?” We don’t — we’re not quite at the point where we can say “This is exactly how you block that pathway from turning into full-blown Parkinson’s disease.” But what I can feel pretty comfortable telling people is that certain steps that will boost your gut health and help with your gut lining will only help you, regardless of your risk for Parkinson’s disease.

Reducing the number of NSAIDs [nonsteroidal anti-inflammatory drugs] like ibuprofen that you take, minimizing alcohol and, to the extent possible, reducing stress — these things all help your gastrointestinal mucosa and help your overall health as well.

Rath: There’s been a big spike in Parkinson’s disease. I didn’t realize this, but globally, apparently, it’s doubled in the last 25 years. Do we have a sense of what is the reason for that spike? And any sense of this new research giving us any clarity on it?

Pasricha: That’s a great question. The number of cases has really doubled in the last 25 years, to the point that some people are calling this a “Parkinson’s pandemic.”

Now, the biggest risk factor is age — we are a population that is aging — and there’s certainly a genetic component, but most people don’t have a known genetic risk. So we start to wonder what are the environmental factors that have been changing over the last several years and decades that could be leading to it? What of those environmental factors might be influencing our stomachs?

One of the environmental factors that we’re very interested in, of course, is food. The way we eat today is certainly not how people ate in the 1950s. It’s certainly not how people ate in the 1850s. We have a lot of different ways that we’re processing food. We’re ultra-processing foods. We’re eating higher-fat foods.

Those things do compromise the gut lining, even in low levels. We don’t yet know the exact role that those things are playing, but it’s certainly one of the factors that we’re looking into.

Rath: We’ve covered so much ground and big questions, but I want to throw in one more big one: Does this have any implications for research on the origins of other neurodegenerative diseases, like Alzheimer’s?

Pasricha: That is a great question, and it’s an important question. Alzheimer’s is also characterized by an abnormal protein in the brain. Alzheimer’s is also associated with a lot of significant GI dysfunction, including constipation.

I think, again, it’s a little bit too early for us to say that we’ve identified GI origins of any of these diseases precisely, but I think this type of study and others like it really do show that the gut and the brain are very closely connected. It’s not — as we often think about the gut-brain connection — just a one-way street in which our brain dictates what’s happening in our gut.

We often think, you know, when we’re nervous or we’re stressed, we get butterflies in our stomach or we suddenly have to go to the bathroom right before our turn at karaoke. That’s one pathway of the gut-brain communication, but we’re really starting to learn how much the influence the gut exerts on the brain in ways that we’re only beginning to understand.

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OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step

OpenAI made the last big breakthrough in artificial intelligence by increasing the size of its models to dizzying proportions, when it introduced GPT-4 last year. The company today announced a new advance that signals a shift in approach—a model that can “reason” logically through many difficult problems and is significantly smarter than existing AI without a major scale-up.

The new model, dubbed OpenAI o1, can solve problems that stump existing AI models, including OpenAI’s most powerful existing model, GPT-4o . Rather than summon up an answer in one step, as a large language model normally does, it reasons through the problem, effectively thinking out loud as a person might, before arriving at the right result.

“This is what we consider the new paradigm in these models,” Mira Murati , OpenAI’s chief technology officer, tells WIRED. “It is much better at tackling very complex reasoning tasks.”

The new model was code-named Strawberry within OpenAI, and it is not a successor to GPT-4o but rather a complement to it, the company says.

Murati says that OpenAI is currently building its next master model, GPT-5, which will be considerably larger than its predecessor. But while the company still believes that scale will help wring new abilities out of AI, GPT-5 is likely to also include the reasoning technology introduced today. “There are two paradigms,” Murati says. “The scaling paradigm and this new paradigm. We expect that we will bring them together.”

LLMs typically conjure their answers from huge neural networks fed vast quantities of training data. They can exhibit remarkable linguistic and logical abilities, but traditionally struggle with surprisingly simple problems such as rudimentary math questions that involve reasoning.

Murati says OpenAI o1 uses reinforcement learning, which involves giving a model positive feedback when it gets answers right and negative feedback when it does not, in order to improve its reasoning process. “The model sharpens its thinking and fine tunes the strategies that it uses to get to the answer,” she says. Reinforcement learning has enabled computers to play games with superhuman skill and do useful tasks like designing computer chips . The technique is also a key ingredient for turning an LLM into a useful and well-behaved chatbot.

Mark Chen, vice president of research at OpenAI, demonstrated the new model to WIRED, using it to solve several problems that its prior model, GPT-4o, cannot. These included an advanced chemistry question and the following mind-bending mathematical puzzle: “A princess is as old as the prince will be when the princess is twice as old as the prince was when the princess’s age was half the sum of their present age. What is the age of the prince and princess?” (The correct answer is that the prince is 30, and the princess is 40).

“The [new] model is learning to think for itself, rather than kind of trying to imitate the way humans would think,” as a conventional LLM does, Chen says.

OpenAI says its new model performs markedly better on a number of problem sets, including ones focused on coding, math, physics, biology, and chemistry. On the American Invitational Mathematics Examination (AIME), a test for math students, GPT-4o solved on average 12 percent of the problems while o1 got 83 percent right, according to the company.

The new model is slower than GPT-4o, and OpenAI says it does not always perform better—in part because, unlike GPT-4o, it cannot search the web and it is not multimodal, meaning it cannot parse images or audio.

Improving the reasoning capabilities of LLMs has been a hot topic in research circles for some time. Indeed, rivals are pursuing similar research lines. In July, Google announced AlphaProof , a project that combines language models with reinforcement learning for solving difficult math problems.

AlphaProof was able to learn how to reason over math problems by looking at correct answers. A key challenge with broadening this kind of learning is that there are not correct answers for everything a model might encounter. Chen says OpenAI has succeeded in building a reasoning system that is much more general. “I do think we have made some breakthroughs there; I think it is part of our edge,” Chen says. “It’s actually fairly good at reasoning across all domains.”

Noah Goodman , a professor at Stanford who has published work on improving the reasoning abilities of LLMs, says the key to more generalized training may involve using a “carefully prompted language model and handcrafted data” for training. He adds that being able to consistently trade the speed of results for greater accuracy would be a “nice advance.”

Yoon Kim , an assistant professor at MIT, says how LLMs solve problems currently remains somewhat mysterious, and even if they perform step-by-step reasoning there may be key differences from human intelligence. This could be crucial as the technology becomes more widely used. “These are systems that would be potentially making decisions that affect many, many people,” he says. “The larger question is, do we need to be confident about how a computational model is arriving at the decisions?”

The technique introduced by OpenAI today also may help ensure that AI models behave well. Murati says the new model has shown itself to be better at avoiding producing unpleasant or potentially harmful output by reasoning about the outcome of its actions. “If you think about teaching children, they learn much better to align to certain norms, behaviors, and values once they can reason about why they’re doing a certain thing,” she says.

Oren Etzioni , a professor emeritus at the University of Washington and a prominent AI expert, says it’s “essential to enable LLMs to engage in multi-step problem solving, use tools, and solve complex problems.” He adds, “Pure scale up will not deliver this.” Etzioni says, however, that there are further challenges ahead. “Even if reasoning were solved, we would still have the challenge of hallucination and factuality.”

OpenAI’s Chen says that the new reasoning approach developed by the company shows that advancing AI need not cost ungodly amounts of compute power. “One of the exciting things about the paradigm is we believe that it’ll allow us to ship intelligence cheaper,” he says, “and I think that really is the core mission of our company.”

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