how to make discussion of results

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

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Writing your Dissertation:  Results and Discussion

When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write.

You may choose to write these sections separately, or combine them into a single chapter, depending on your university’s guidelines and your own preferences.

There are advantages to both approaches.

Writing the results and discussion as separate sections allows you to focus first on what results you obtained and set out clearly what happened in your experiments and/or investigations without worrying about their implications.This can focus your mind on what the results actually show and help you to sort them in your head.

However, many people find it easier to combine the results with their implications as the two are closely connected.

Check your university’s requirements carefully before combining the results and discussions sections as some specify that they must be kept separate.

Results Section

The Results section should set out your key experimental results, including any statistical analysis and whether or not the results of these are significant.

You should cover any literature supporting your interpretation of significance. It does not have to include everything you did, particularly for a doctorate dissertation. However, for an undergraduate or master's thesis, you will probably find that you need to include most of your work.

You should write your results section in the past tense: you are describing what you have done in the past.

Every result included MUST have a method set out in the methods section. Check back to make sure that you have included all the relevant methods.

Conversely, every method should also have some results given so, if you choose to exclude certain experiments from the results, make sure that you remove mention of the method as well.

If you are unsure whether to include certain results, go back to your research questions and decide whether the results are relevant to them. It doesn’t matter whether they are supportive or not, it’s about relevance. If they are relevant, you should include them.

Having decided what to include, next decide what order to use. You could choose chronological, which should follow the methods, or in order from most to least important in the answering of your research questions, or by research question and/or hypothesis.

You also need to consider how best to present your results: tables, figures, graphs, or text. Try to use a variety of different methods of presentation, and consider your reader: 20 pages of dense tables are hard to understand, as are five pages of graphs, but a single table and well-chosen graph that illustrate your overall findings will make things much clearer.

Make sure that each table and figure has a number and a title. Number tables and figures in separate lists, but consecutively by the order in which you mention them in the text. If you have more than about two or three, it’s often helpful to provide lists of tables and figures alongside the table of contents at the start of your dissertation.

Summarise your results in the text, drawing on the figures and tables to illustrate your points.

The text and figures should be complementary, not repeat the same information. You should refer to every table or figure in the text. Any that you don’t feel the need to refer to can safely be moved to an appendix, or even removed.

Make sure that you including information about the size and direction of any changes, including percentage change if appropriate. Statistical tests should include details of p values or confidence intervals and limits.

While you don’t need to include all your primary evidence in this section, you should as a matter of good practice make it available in an appendix, to which you should refer at the relevant point.

For example:

Details of all the interview participants can be found in Appendix A, with transcripts of each interview in Appendix B.

You will, almost inevitably, find that you need to include some slight discussion of your results during this section. This discussion should evaluate the quality of the results and their reliability, but not stray too far into discussion of how far your results support your hypothesis and/or answer your research questions, as that is for the discussion section.

See our pages: Analysing Qualitative Data and Simple Statistical Analysis for more information on analysing your results.

Discussion Section

This section has four purposes, it should:

• Interpret and explain your results
• Answer your research question
• Justify your approach
• Critically evaluate your study

The discussion section therefore needs to review your findings in the context of the literature and the existing knowledge about the subject.

You also need to demonstrate that you understand the limitations of your research and the implications of your findings for policy and practice. This section should be written in the present tense.

The Discussion section needs to follow from your results and relate back to your literature review . Make sure that everything you discuss is covered in the results section.

Some universities require a separate section on recommendations for policy and practice and/or for future research, while others allow you to include this in your discussion, so check the guidelines carefully.

Starting the Task

Most people are likely to write this section best by preparing an outline, setting out the broad thrust of the argument, and how your results support it.

You may find techniques like mind mapping are helpful in making a first outline; check out our page: Creative Thinking for some ideas about how to think through your ideas. You should start by referring back to your research questions, discuss your results, then set them into the context of the literature, and then into broader theory.

This is likely to be one of the longest sections of your dissertation, and it’s a good idea to break it down into chunks with sub-headings to help your reader to navigate through the detail.

Fleshing Out the Detail

Once you have your outline in front of you, you can start to map out how your results fit into the outline.

This will help you to see whether your results are over-focused in one area, which is why writing up your research as you go along can be a helpful process. For each theme or area, you should discuss how the results help to answer your research question, and whether the results are consistent with your expectations and the literature.

The Importance of Understanding Differences

If your results are controversial and/or unexpected, you should set them fully in context and explain why you think that you obtained them.

Your explanations may include issues such as a non-representative sample for convenience purposes, a response rate skewed towards those with a particular experience, or your own involvement as a participant for sociological research.

You do not need to be apologetic about these, because you made a choice about them, which you should have justified in the methodology section. However, you do need to evaluate your own results against others’ findings, especially if they are different. A full understanding of the limitations of your research is part of a good discussion section.

At this stage, you may want to revisit your literature review, unless you submitted it as a separate submission earlier, and revise it to draw out those studies which have proven more relevant.

Conclude by summarising the implications of your findings in brief, and explain why they are important for researchers and in practice, and provide some suggestions for further work.

You may also wish to make some recommendations for practice. As before, this may be a separate section, or included in your discussion.

The results and discussion, including conclusion and recommendations, are probably the most substantial sections of your dissertation. Once completed, you can begin to relax slightly: you are on to the last stages of writing!

Continue to: Dissertation: Conclusion and Extras Writing your Methodology

See also: Writing a Literature Review Writing a Research Proposal Academic Referencing What Is the Importance of Using a Plagiarism Checker to Check Your Thesis?

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Writing a scientific paper.

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Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

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This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

• Factual statements supported by evidence. Short and sweet without excess words
• Present representative data rather than endlessly repetitive data
• Discuss variables only if they had an effect (positive or negative)
• Use meaningful statistics
• Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

• Present the results of the paper, in logical order, using tables and graphs as necessary.
• Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
• Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
• Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
• Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
• It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
• You must refer in the text to each figure or table you include in your paper.
• Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
• Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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How to Start a Discussion Section in Research? [with Examples]

The examples below are from 72,017 full-text PubMed research papers that I analyzed in order to explore common ways to start writing the Discussion section.

Research papers included in this analysis were selected at random from those uploaded to PubMed Central between the years 2016 and 2021. Note that I used the BioC API to download the data (see the References section below).

Examples of how to start writing the Discussion section

In the Discussion section, you should explain the meaning of your results, their importance, and implications. [for more information, see: How to Write & Publish a Research Paper: Step-by-Step Guide ]

The Discussion section can:

1. Start by restating the study objective

“ The purpose of this study was to investigate the relationship between muscle synergies and motion primitives of the upper limb motions.” Taken from the Discussion section of this article on PubMed

“ The main objective of this study was to identify trajectories of autonomy.” Taken from the Discussion section of this article on PubMed

“ In the present study, we investigated the whole brain regional homogeneity in patients with melancholic MDD and non-melancholic MDD at rest . “ Taken from the Discussion section of this article on PubMed

2. Start by mentioning the main finding

“ We found that autocracy and democracy have acted as peaks in an evolutionary landscape of possible modes of institutional arrangements.” Taken from the Discussion section of this article on PubMed

“ In this study, we demonstrated that the neural mechanisms of rhythmic movements and skilled movements are similar.” Taken from the Discussion section of this article on PubMed

“ The results of this study show that older adults are a diverse group concerning their activities on the Internet.” Taken from the Discussion section of this article on PubMed

3. Start by pointing out the strength of the study

“ To our knowledge, this investigation is by far the largest epidemiological study employing real-time PCR to study periodontal pathogens in subgingival plaque.” Taken from the Discussion section of this article on PubMed

“ This is the first human subject research using the endoscopic hemoglobin oxygen saturation imaging technology for patients with aero-digestive tract cancers or adenomas.” Taken from the Discussion section of this article on PubMed

“ In this work, we introduced a new real-time flow imaging method and systematically demonstrated its effectiveness with both flow phantom experiments and in vivo experiments.” Taken from the Discussion section of this article on PubMed

Most used words at the start of the Discussion

Here are the top 10 phrases used to start a discussion section in our dataset:

• Comeau DC, Wei CH, Islamaj Doğan R, and Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing,  Bioinformatics , btz070, 2019.

Further reading

• How Long Should the Discussion Section Be? Data from 61,517 Examples
• How to Write & Publish a Research Paper: Step-by-Step Guide
• “I” & “We” in Academic Writing: Examples from 9,830 Studies

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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• 12 March, 2021
• Academic Writing Skills

In the results section of your academic paper, you present what you found when you conducted your analyses, whereas in your discussion section you explain what your results mean and connect them to prior research studies. In other words, the results section is where you describe what you did, and the discussion sections is where you describe what this means for the field.

The results section should include the findings of your study without any interpretations or implications that you can draw from those results. Here, you present the findings using text supported by tables, charts, graphs and other figures. For example, in the following excerpt from article by Tolksdorf, Crawshaw, & Rohlfing, (2021), you can see how directly they report the results of their study.

Contrary to our hypothesis, there was no main effect of time, F(3, ∞) = 0.638, p = 0.166, and no significant interaction between experimental condition and time, F(3, ∞) = 0.427, p = 0.133, indicating that no significant changes in children's social referencing behavior were found in either group over the entire course of the sessions, including all learning and test situations. However, there was a highly significant main effect of condition F(1, 16.99) = 49.08, p < 0.001, demonstrating that children in the human condition displayed social referencing significantly more often than their peers interacting with the robotic partner. (p. 6)

Further in the results section the authors use a table to illustrate their results.

Table 1 presents an overview of the different interactional contexts in which children’s social referencing was situated during the long-term interaction. (p. 6)

As you can see, the results section is very direct and reports the outcome from the statistical analyses conducted. Tables and figures can help break up this section, as it can be very technical. In addition, using visuals in this way makes the results more accessible to readers.

The discussion section, which follows the results section, will include an explanation of the results. In this section, you should connect your results to previous research studies, make explicit connections back to your research question(s) and include an explanation about how the results might be generalized. This is where you make an argument that supports your main conclusions. Unlike the results section, the discussion section is where you interpret your results and explain what they mean, draw implications from your results and articulate why they matter, discuss any limitations of your results, and provide recommendations that can be made from these results. The following excerpts from the Tolksdorf, Crawshaw, & Rohlfing, (2021), help to further illustrate the difference between the results and discussions sections.

Contrary to our prior assumption, we could not observe a significant decrease in children’s social referencing in both groups despite the repetition of the interaction and increasing familiarity with the situation. Whereas, there appeared to be a slight decreasing tendency from the second to the third learning situation in each group, this trend may have been slowed down by the subsequent novel situation of the retention task, which again increased children’s reliance on the caregiver despite increasing familiarity with the interaction partner. (p. 8)

The large difference in children’s social referencing behavior between an interaction with the human vs. robotic partner is striking. One explanation for our findings is that a human partner naturally responds to various social cues (Kahle and Argyle, 2014) from the child in ways that social robots are not yet capable of, given their present technological limitations. (p. 8)

Notice how the authors provide a critical analysis of their results and offer explanations for what they found. In the second excerpt, observe how they tie an explanation for their result to prior research conducted in the field. Focusing on the results and discussion sections of different articles, and highlighting language that differentiates these sections from each other, can really help you to write your academic papers effectively.

Although the length and structure of the discussion section across research papers varies, there are some commonalities in the structure and content of these sections. Below is a suggested outline for a discussion section.

Paragraph 1.

In this paragraph provide a broad overview of the importance of your study. This is where you should restate your research topic. Avoid just repeating what you included in the results section. Include the main research findings that answer your primary research question(s).

Paragraph 2–3.

This section should be a critical analysis of your major findings. Here, you should articulate your interpretations of those findings. You should include whether these were the findings you expected and also whether they support any hypothesis you had. Provide explanations for the significance of the results and for any unexpected findings. Link your primary findings back to prior research studies. This section would also include any implications of your results. 

Paragraph 4.

Here you would include a discussion of any secondary findings that are of note. Additionally, you would also include any limitations of your study and how future studies might mitigate these limitations. The excerpt below, from the Tolksdorf, Crawshaw, & Rohlfing, (2021) study, provides an example of this.

We would also like to point to the possibility that the study design and procedure could have impacted our results. Adapting the design of the interaction from the robot experimental setting to be suitably comparable when taking place with a human interaction partner required us to make certain decisions. (p. 9)

Paragraph 5.

This should include the conclusion of the discussion section, and future directions. In this section you could include any new research questions that arose as a result of your study. Implications from your findings for the field should also be discussed in this paragraph.

There are a number of common errors researchers make when writing the results and discussion sections. The following checklist can help you avoid these common mistakes.

 Do not include interpretations or explanations of the findings in your results section. Remember that in the results section you are telling the reader what you found and in the discussion section you are telling them what it means and why it matters.

 Do not exclude negative findings from your results section. Although the temptation is to report only positive findings, negative findings are important to other researchers.

 You should not introduce any findings in your discussion section that were not included in the results section. These two sections should align, and you should discuss and explain only what you have already reported.

 Don’t restate results in the discussion paper without an explanation or critical analysis of what they mean and why they matter.

 Don’t forget to go back and check that these two sections align, and the flow from the results section to the discussion section is smooth and clear.

Tolksdorf NF, Crawshaw CE and Rohlfing KJ (2021) Comparing the Effects of a Different Social Partner (Social Robot vs. Human) on Children's Social Referencing in Interaction. Front. Educ. 5:569615. doi: 10.3389/feduc.2020.569615

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Results & Discussion

Characteristics of results & discussion.

• Results section contains data collected by scientists from experiments that they conducted.
• Data can be measurements, numbers, descriptions and/or observations.
• Scientific data is typically described using graphs, tables, figures, diagrams, maps, charts, photographs and/or equations.
• Discussion section provides an interpretation of the data, especially in context to previously published research.

The Results and Discussion sections can be written as separate sections (as shown in Fig. 2 ), but are often combined in a poster into one section called Results and Discussion.   This is done in order to (1) save precious space on a poster for the many pieces of information that a scientist would like to tell their audience and (2) by combining the two sections, it becomes easier for the audience to understand the significance of the research.   Combining the Results section and Discussion section in a poster is different for what is typically done for a scientific journal article.   In most journal articles, the Results section is separated from the Discussion section.   Journal articles are different from posters in that a scientist is not standing next to their journal article explaining it to a reader.   Therefore, in a journal article, an author needs to provide more detailed information so that the reader can understand the research independently.   Separating the Results section and Discussion section allows an author the space necessary to write a lengthier description of the research. Journal articles typically contain more text and more content (e.g., figures, tables) than posters.

The Results and Discussion section should contain data, typically in the form of a graph, histogram, chart, image, color-coded map or table ( Figs. 1 & 4 ).   Very often data means numbers that scientists collect from making measurements.   These data are typically presented to an audience in the form of graphs and charts to show a reader how these numbers change over time, space or experimental conditions ( Fig. 7 ).   Numbers can increase, decrease or stay the same and a graph, or another type of figure, can be effectively used to convey this information to a reader in a visual format ( Fig. 7 ).      

Figure 7. Example of a Graph

An audience will be attracted to a poster because of its figures and so it is very important for the author to pay particular attention to the creation, design and placement of the figures in a poster ( Figs. 1 & 4 ).   A good figure is one that is informative, easy to comprehend and allows the reader to understand the significance of the data and experiment.   Very often an author will use color to draw attention to a figure.      

The Discussion section should state the importance of the research that is presented in the poster.   It should provide an interpretation of the results, especially in context to previously published research.   It may propose future experiments that need to be conducted as a result of the research presented in the poster.   It should clearly illustrate the significance of the research with regards to new knowledge, understanding and/or discoveries that were made as part of the research.

Scientific Posters: A Learner's Guide Copyright © 2020 by Ella Weaver; Kylienne A. Shaul; Henry Griffy; and Brian H. Lower is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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What is decision making?

Decisions, decisions. When was the last time you struggled with a choice? Maybe it was this morning, when you decided to hit the snooze button—again. Perhaps it was at a restaurant, with a miles-long menu and the server standing over you. Or maybe it was when you left your closet in a shambles after trying on seven different outfits before a big presentation. Often, making a decision—even a seemingly simple one—can be difficult. And people will go to great lengths—and pay serious sums of money—to avoid having to make a choice. The expensive tasting menu at the restaurant, for example. Or limiting your closet choices to black turtlenecks, à la Steve Jobs.

Get to know and directly engage with senior McKinsey experts on decision making

Aaron De Smet is a senior partner in McKinsey’s New Jersey office, Eileen Kelly Rinaudo  is McKinsey’s global director of advancing women executives and is based in the New York office, Frithjof Lund is a senior partner in the Oslo office, and Leigh Weiss is a senior adviser in the Boston office.

If you’ve ever wrestled with a decision at work, you’re definitely not alone. According to McKinsey research, executives spend a significant portion of their time— nearly 40 percent , on average—making decisions. Worse, they believe most of that time is poorly used. People struggle with decisions so much so that we actually get exhausted from having to decide too much, a phenomenon called decision fatigue.

But decision fatigue isn’t the only cost of ineffective decision making. According to a McKinsey survey of more than 1,200 global business leaders, inefficient decision making costs a typical Fortune 500 company 530,000 days  of managers’ time each year, equivalent to about $250 million in annual wages. That’s a lot of turtlenecks.

How can business leaders ease the burden of decision making and put this time and money to better use? Read on to learn the ins and outs of smart decision making—and how to put it to work.

Learn more about our People & Organizational Performance Practice .

How can organizations untangle ineffective decision-making processes?

McKinsey research has shown that agile is the ultimate solution for many organizations looking to streamline their decision making . Agile organizations are more likely to put decision making in the right hands, are faster at reacting to (or anticipating) shifts in the business environment, and often attract top talent who prefer working at companies with greater empowerment and fewer layers of management.

For organizations looking to become more agile, it’s possible to quickly boost decision-making efficiency by categorizing the type of decision to be made and adjusting the approach accordingly. In the next section, we review three types of decision making and how to optimize the process for each.

What are three keys to faster, better decisions?

Business leaders today have access to more sophisticated data than ever before. But it hasn’t necessarily made decision making any easier. For one thing, organizational dynamics—such as unclear roles, overreliance on consensus, and death by committee—can get in the way of straightforward decision making. And more data often means more decisions to be taken, which can become too much for one person, team, or department. This can make it more difficult for leaders to cleanly delegate, which in turn can lead to a decline in productivity.

Leaders are growing increasingly frustrated with broken decision-making processes, slow deliberations, and uneven decision-making outcomes. Fewer than half  of the 1,200 respondents of a McKinsey survey report that decisions are timely, and 61 percent say that at least half the time they spend making decisions is ineffective.

What’s the solution? According to McKinsey research, effective solutions center around categorizing decision types and organizing different processes to support each type. Further, each decision category should be assigned its own practice—stimulating debate, for example, or empowering employees—to yield improvements in effectiveness.

Here are the three decision categories  that matter most to senior leaders, and the standout practice that makes the biggest difference for each type of decision.

• Big-bet decisions are infrequent but high risk, such as acquisitions. These decisions carry the potential to shape the future of the company, and as a result are generally made by top leaders and the board. Spurring productive debate by assigning someone to argue the case for and against a potential decision can improve big-bet decision making.
• Cross-cutting decisions, such as pricing, can be frequent and high risk. These are usually made by business unit heads, in cross-functional forums as part of a collaborative process. These types of decisions can be improved by doubling down on process refinement. The ideal process should be one that helps clarify objectives, measures, and targets.
• Delegated decisions are frequent but low risk and are handled by an individual or working team with some input from others. Delegated decision making can be improved by ensuring that the responsibility for the decision is firmly in the hands of those closest to the work. This approach also enhances engagement and accountability.

In addition, business leaders can take the following four actions to help sustain rapid decision making :

• Focus on the game-changing decisions, ones that will help an organization create value and serve its purpose.
• Convene only necessary meetings, and eliminate lengthy reports. Turn unnecessary meetings into emails, and watch productivity bloom. For necessary meetings, provide short, well-prepared prereads to aid in decision making.
• Clarify the roles of decision makers and other voices. Who has a vote, and who has a voice?
• Push decision-making authority to the front line—and tolerate mistakes.

Introducing McKinsey Explainers : Direct answers to complex questions

How can business leaders effectively delegate decision making.

Business is more complex and dynamic than ever, meaning business leaders are faced with needing to make more decisions in less time. Decision making takes up an inordinate amount of management’s time—up to 70 percent for some executives—which leads to inefficiencies and opportunity costs.

As discussed above, organizations should treat different types of decisions differently . Decisions should be classified  according to their frequency, risk, and importance. Delegated decisions are the most mysterious for many organizations: they are the most frequent, and yet the least understood. Only about a quarter of survey respondents  report that their organizations make high-quality and speedy delegated decisions. And yet delegated decisions, because they happen so often, can have a big impact on organizational culture.

The key to better delegated decisions is to empower employees by giving them the authority and confidence to act. That means not simply telling employees which decisions they can or can’t make; it means giving employees the tools they need to make high-quality decisions and the right level of guidance as they do so.

Here’s how to support delegation and employee empowerment:

• Ensure that your organization has a well-defined, universally understood strategy. When the strategic intent of an organization is clear, empowerment is much easier because it allows teams to pull in the same direction.
• Clearly define roles and responsibilities. At the foundation of all empowerment efforts is a clear understanding of who is responsible for what, including who has input and who doesn’t.
• Invest in capability building (and coaching) up front. To help managers spend meaningful coaching time, organizations should also invest in managers’ leadership skills.
• Build an empowerment-oriented culture. Leaders should role model mindsets that promote empowerment, and managers should build the coaching skills they want to see. Managers and employees, in particular, will need to get comfortable with failure as a necessary step to success.
• Decide when to get involved. Managers should spend effort up front to decide what is worth their focused attention. They should know when it’s appropriate to provide close guidance and when not to.

How can you guard against bias in decision making?

Cognitive bias is real. We all fall prey, no matter how we try to guard ourselves against it. And cognitive and organizational bias undermines good decision making, whether you’re choosing what to have for lunch or whether to put in a bid to acquire another company.

Here are some of the most common cognitive biases and strategies for how to avoid them:

• Confirmation bias. Often, when we already believe something, our minds seek out information to support that belief—whether or not it is actually true. Confirmation bias  involves overweighting evidence that supports our belief, underweighting evidence against our belief, or even failing to search impartially for evidence in the first place. Confirmation bias is one of the most common traps organizational decision makers fall into. One famous—and painful—example of confirmation bias is when Blockbuster passed up the opportunity  to buy a fledgling Netflix for $50 million in 2000. (Actually, that’s putting it politely. Netflix executives remember being “laughed out” of Blockbuster’s offices.) Fresh off the dot-com bubble burst of 2000, Blockbuster executives likely concluded that Netflix had approached them out of desperation—not that Netflix actually had a baby unicorn on its hands.
• Herd mentality. First observed by Charles Mackay in his 1841 study of crowd psychology, herd mentality happens when information that’s available to the group is determined to be more useful than privately held knowledge. Individuals buy into this bias because there’s safety in the herd. But ignoring competing viewpoints might ultimately be costly. To counter this, try a teardown exercise , wherein two teams use scenarios, advanced analytics, and role-playing to identify how a herd might react to a decision, and to ensure they can refute public perceptions.
• Sunk-cost fallacy. Executives frequently hold onto underperforming business units or projects because of emotional or legacy attachment . Equally, business leaders hate shutting projects down . This, researchers say, is due to the ingrained belief that if everyone works hard enough, anything can be turned into gold. McKinsey research indicates two techniques for understanding when to hold on and when to let go. First, change the burden of proof from why an asset should be cut to why it should be retained. Next, categorize business investments according to whether they should be grown, maintained, or disposed of—and follow clearly differentiated investment rules  for each group.
• Ignoring unpleasant information. Researchers call this the “ostrich effect”—when people figuratively bury their heads in the sand , ignoring information that will make their lives more difficult. One study, for example, found that investors were more likely to check the value of their portfolios when the markets overall were rising, and less likely to do so when the markets were flat or falling. One way to help get around this is to engage in a readout process, where individuals or teams summarize discussions as they happen. This increases the likelihood that everyone leaves a meeting with the same understanding of what was said.
• Halo effect. Important personal and professional choices are frequently affected by people’s tendency to make specific judgments based on general impressions . Humans are tempted to use simple mental frames to understand complicated ideas, which means we frequently draw conclusions faster than we should. The halo effect is particularly common in hiring decisions. To avoid this bias, structured interviews can help mitigate the essentializing tendency. When candidates are measured against indicators, intuition is less likely to play a role.

For more common biases and how to beat them, check out McKinsey’s Bias Busters Collection .

Learn more about Strategy & Corporate Finance consulting  at McKinsey—and check out job opportunities related to decision making if you’re interested in working at McKinsey.

Articles referenced include:

• “ Bias busters: When the crowd isn’t necessarily wise ,” McKinsey Quarterly , May 23, 2022, Eileen Kelly Rinaudo , Tim Koller , and Derek Schatz
• “ Boards and decision making ,” April 8, 2021, Aaron De Smet , Frithjof Lund , Suzanne Nimocks, and Leigh Weiss
• “ To unlock better decision making, plan better meetings ,” November 9, 2020, Aaron De Smet , Simon London, and Leigh Weiss
• “ Reimagine decision making to improve speed and quality ,” September 14, 2020, Julie Hughes , J. R. Maxwell , and Leigh Weiss
• “ For smarter decisions, empower your employees ,” September 9, 2020, Aaron De Smet , Caitlin Hewes, and Leigh Weiss
• “ Bias busters: Lifting your head from the sand ,” McKinsey Quarterly , August 18, 2020, Eileen Kelly Rinaudo
• “ Decision making in uncertain times ,” March 24, 2020, Andrea Alexander, Aaron De Smet , and Leigh Weiss
• “ Bias busters: Avoiding snap judgments ,” McKinsey Quarterly , November 6, 2019, Tim Koller , Dan Lovallo, and Phil Rosenzweig
• “ Three keys to faster, better decisions ,” McKinsey Quarterly , May 1, 2019, Aaron De Smet , Gregor Jost , and Leigh Weiss
• “ Decision making in the age of urgency ,” April 30, 2019, Iskandar Aminov, Aaron De Smet , Gregor Jost , and David Mendelsohn
• “ Bias busters: Pruning projects proactively ,” McKinsey Quarterly , February 6, 2019, Tim Koller , Dan Lovallo, and Zane Williams
• “ Decision making in your organization: Cutting through the clutter ,” McKinsey Quarterly , January 16, 2018, Aaron De Smet , Simon London, and Leigh Weiss
• “ Untangling your organization’s decision making ,” McKinsey Quarterly , June 21, 2017, Aaron De Smet , Gerald Lackey, and Leigh Weiss
• “ Are you ready to decide? ,” McKinsey Quarterly , April 1, 2015, Philip Meissner, Olivier Sibony, and Torsten Wulf.

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• Open access
• Published: 03 June 2024

The use of evidence to guide decision-making during the COVID-19 pandemic: divergent perspectives from a qualitative case study in British Columbia, Canada

• Laura Jane Brubacher   ORCID: orcid.org/0000-0003-2806-9539 1 , 2 ,
• Chris Y. Lovato 1 ,
• Veena Sriram 1 , 3 ,
• Michael Cheng 1 &
• Peter Berman 1  

Health Research Policy and Systems volume  22 , Article number:  66 ( 2024 ) Cite this article

Metrics details

The challenges of evidence-informed decision-making in a public health emergency have never been so notable as during the COVID-19 pandemic. Questions about the decision-making process, including what forms of evidence were used, and how evidence informed—or did not inform—policy have been debated.

We examined decision-makers' observations on evidence-use in early COVID-19 policy-making in British Columbia (BC), Canada through a qualitative case study. From July 2021- January 2022, we conducted 18 semi-structured key informant interviews with BC elected officials, provincial and regional-level health officials, and civil society actors involved in the public health response. The questions focused on: (1) the use of evidence in policy-making; (2) the interface between researchers and policy-makers; and (3) key challenges perceived by respondents as barriers to applying evidence to COVID-19 policy decisions. Data were analyzed thematically, using a constant comparative method. Framework analysis was also employed to generate analytic insights across stakeholder perspectives.

Overall, while many actors’ impressions were that BC's early COVID-19 policy response was evidence-informed, an overarching theme was a lack of clarity and uncertainty as to what evidence was used and how it flowed into decision-making processes. Perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform pandemic governance. Respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy.

Conclusions

Areas to be considered in planning for future emergencies include: information flow between policy-makers and researchers, coordination of data collection and use, and transparency as to how decisions are made—all of which reflect a need to improve communication. Based on our findings, clear mechanisms and processes for channeling varied forms of evidence into decision-making need to be identified, and doing so will strengthen preparedness for future public health crises.

Peer Review reports

The challenges of evidence-informed decision-making Footnote 1 in a public health emergency have never been so salient as during the COVID-19 pandemic, given its unprecedented scale, rapidly evolving virology, and multitude of global information systems to gather, synthesize, and disseminate evidence on the SARS-CoV-2 virus and associated public health and social measures [ 1 , 2 , 3 ]. Early in the COVID-19 pandemic, rapid decision-making became central for governments globally as they grappled with crucial decisions for which there was limited evidence. Critical questions exist, in looking retrospectively at these decision-making processes and with an eye to strengthening future preparedness: Were decisions informed by 'evidence'? What forms of evidence were used, and how, by decision-makers? [ 4 , 5 , 6 ].

Scientific evidence, including primary research, epidemiologic research, and knowledge synthesis, is one among multiple competing influences that inform decision-making processes in an outbreak such as COVID-19 [ 7 ]. Indeed, the use of multiple forms of evidence has been particularly notable as it applies to COVID-19 policy-making. Emerging research has also documented the important influence of ‘non-scientific’ evidence such as specialized expertise and experience, contextual information, and level of available resources [ 8 , 9 , 10 ]. The COVID-19 pandemic has underscored the politics of evidence-use in policy-making [ 11 ]; what evidence is used and how can be unclear, and shaped by political bias [ 4 , 5 ]. Moreover, while many governments have established scientific advisory boards, the perspectives of these advisors were reportedly largely absent from COVID-19 policy processes [ 6 ]. How evidence and public health policy interface—and intersect—is a complex question, particularly in the dynamic context of a public health emergency.

Within Canada, a hallmark of the public health system and endorsed by government is evidence-informed decision-making [ 12 ]. In British Columbia (BC), Canada, during the early phases of COVID-19 (March—June 2020), provincial public health communication focused primarily on voluntary compliance with recommended public health and social measures, and on supporting those most affected by the pandemic. Later, the response shifted from voluntary compliance to mandatory enforceable government orders [ 13 ]. Like many other jurisdictions, the government’s public messaging in BC asserted that the province took an approach to managing the COVID-19 pandemic and developing related policy that was based on scientific evidence, specifically. For example, in March 2021, in announcing changes to vaccination plans, Dr. Bonnie Henry, the Provincial Health Officer, stated, " This is science in action " [ 14 ]. As a public health expert with scientific voice, the Provincial Health Officer has been empowered to speak on behalf of the BC government across the COVID-19 pandemic progression. While this suggests BC is a jurisdiction which has institutionalized scientifically-informed decision-making as a core tenet of effective public health crisis response, it remains unclear as to whether BC’s COVID-19 response could, in fact, be considered evidence-informed—particularly from the perspectives of those involved in pandemic decision-making and action. Moreover, if evidence-informed, what types of evidence were utilized and through what mechanisms, how did this evidence shape decision-making, and what challenges existed in moving evidence to policy and praxis in BC’s COVID-19 response?

The objectives of this study were: (1) to explore and characterize the perspectives of BC actors involved in the COVID-19 response with respect to evidence-use in COVID-19 decision-making; and (2) to identify opportunities for and barriers to evidence-informed decision-making in BC’s COVID-19 response, and more broadly. This inquiry may contribute to identifying opportunities for further strengthening the synthesis and application of evidence (considered broadly) to public health policy and decision-making, particularly in the context of future public health emergencies, both in British Columbia and other jurisdictions.

Study context

This qualitative study was conducted in the province of British Columbia (BC), Canada, a jurisdiction with a population of approximately five million people [ 15 ]. Within BC’s health sector, key actors involved in the policy response to COVID-19 included: elected officials, the BC Government’s Ministry of Health (MOH), the Provincial Health Services Authority (PHSA), Footnote 2 the Office of the Provincial Health Officer (PHO), Footnote 3 the BC Centre for Disease Control (BCCDC), Footnote 4 and Medical Health Officers (MHOs) and Chief MHOs at regional and local levels.

Health research infrastructure within the province includes Michael Smith Health Research BC [ 16 ] and multiple post-secondary research and education institutions (e.g., The University of British Columbia). Unlike other provincial (e.g., Ontario) and international (e.g., UK) jurisdictions, BC did not establish an independent, formal scientific advisory panel or separate organizational structure for public health intelligence in COVID-19. That said, a Strategic Research Advisory Council was established, reporting to the MOH and PHO, to identify COVID-19 research gaps and commission needed research for use within the COVID-19 response [ 17 ].

This research was part of a multidisciplinary UBC case study investigating the upstream determinants of the COVID-19 response in British Columbia, particularly related to institutions, politics, and organizations and how these interfaced with, and affected, pandemic governance [ 18 ]. Ethics approval for this study was provided by the University of British Columbia (UBC)’s Institutional Research Ethics Board (Certificate #: H20-02136).

Data collection

From July 2021 to January 2022, 18 semi-structured key informant interviews were conducted with BC elected officials, provincial and regional-level health officials, and civil society actors (e.g., within non-profit research organizations, unions) (Table 1 ). Initially, respondents were purposively sampled, based on their involvement in the COVID-19 response and their positioning within the health system organizational structure. Snowball sampling was used to identify additional respondents, with the intent of representing a range of organizational roles and actor perspectives. Participants were recruited via email invitation and provided written informed consent to participate.

Interviews were conducted virtually using Zoom® videoconferencing, with the exception of one hybrid in-person/Zoom® interview. Each interview was approximately one hour in duration. One to two research team members led each interview. The full interview protocol focused on actors’ descriptions of decision-making processes across the COVID-19 pandemic progression, from January 2020 to the date of the interviews, and they were asked to identify key decision points (e.g., emergency declaration, business closures) [see Additional File 1 for the full semi-structured interview guide]. For this study, we used a subset of interview questions focused on evidence-use in the decision-making process, and the organizational structures or actors involved, in BC's early COVID-19 pandemic response (March–August 2020). Questions were adapted to be relevant to a respondent’s expertise and particular involvement in the response. ‘Evidence’ was left undefined and considered broadly by the research team (i.e., both ‘scientific’/research-based and ‘non-scientific’ inputs) within interview questions, and therefore at the discretion of the participant as to what inputs they perceived and described as ‘evidence’ that informed or did not inform pandemic decision-making. Interviews were audio-recorded over Zoom® with permission and transcribed using NVivo Release 1.5© software. Each transcript was then manually verified for accuracy by 1–2 members of the research team.

Data analysis

An inductive thematic analysis was conducted, using a constant comparative method, to explore points of divergence and convergence across interviews and stakeholder perspectives [ 19 ]. Transcripts were inductively coded in NVivo Release 1.5© software, which was used to further organize and consolidate codes, generate a parsimonious codebook to fit the data, and retrieve interview excerpts [ 20 ]. Framework analysis was also employed as an additional method for generating analytic insights across stakeholder perspectives and contributed to refining the overall coding [ 21 ]. Triangulation across respondents and analytic methods, as well as team collaboration in reviewing and refining the codebook, contributed to validity of the analysis [ 22 ].

How did evidence inform early COVID-19 policy-making in BC?

Decision-makers described their perceptions on the use of evidence in policy-making; the interface between researchers and policy-makers; and specific barriers to evidence-use in policy-making within BC’s COVID-19 response. In discussing the use of evidence, respondents focused on ‘scientific’ evidence; however, they noted a lack of clarity as to how and what evidence flowed into decision-making. They also acknowledged that ‘scientific’ evidence was one of multiple factors influencing decisions. The themes described below reflect the narrative underlying their perspectives.

Perceptions of evidence-use

Multiple provincial actors generally expressed confidence or had an overall impression that decisions were evidence-based (IDI5,9), stating definitively that, "I don’t think there was a decision we made that wasn’t evidence-informed" (IDI9) and that "the science became a driver of decisions that were made" (IDI5). However, at the regional health authority level, one actor voiced skepticism that policy decisions were consistently informed by scientific evidence specifically, stating, "a lot of decisions [the PHO] made were in contrast to science and then shifted to be by the science" ( IDI6). The evolving nature of the available evidence and scientific understanding of the virus throughout the pandemic was acknowledged. For instance, one actor stated that, "I’ll say the response has been driven by the science; the science has been changing…from what I’ve seen, [it] has been a very science-based response" (IDI3).

Some actors narrowed in on certain policy decisions they believed were or were not evidence-informed. Policy decisions in 2020 that actors believed were directly informed by scientific data included the early decision to restrict informal, household gatherings; to keep schools open for in-person learning; to implement a business safety plan requirement across the province; and to delay the second vaccine dose for maximum efficacy. One provincial public health actor noted that an early 2020 decision made, within local jurisdictions, to close playgrounds was not based on scientific evidence. Further, the decision prompted public health decision-makers to centralize some decision-making to the provincial level, to address decisions being made 'on the ground' that were not based on scientific evidence (IDI16). Similarly, they added that the policy decision to require masking in schools was not based on scientific evidence; rather, "it's policy informed by the noise of your community." As parents and other groups within the community pushed for masking, this was "a policy decision to help schools stay open."

Early in the pandemic response, case data in local jurisdictions were reportedly used for monitoring and planning. These "numerator data" (IDI1), for instance case or hospitalization counts, were identified as being the primary mode of evidence used to inform decisions related to the implementation or easing of public health and social measures. The ability to generate epidemiological count data early in the pandemic due to efficient scaling up of PCR testing for COVID-19 was noted as a key advantage (IDI16). As the pandemic evolved in 2020, however, perspectives diverged in relation to the type of data that decision-makers relied on. For example, it was noted that BCCDC administered an online, voluntary survey to monitor unintended consequences of public health and social measures and inform targeted interventions. Opinions varied on whether this evidence was successfully applied in decision-making. One respondent emphasized this lack of application of evidence and perceived that public health orders were not informed by the level and type of evidence available, beyond case counts: "[In] a communicable disease crisis like a pandemic, the collateral impact slash damage is important and if you're going to be a public health institute, you actually have to bring those to the front, not just count cases" (IDI1).

There also existed some uncertainty and a perceived lack of transparency or clarity as to how or whether data analytic ‘entities’, such as BCCDC or research institutions, fed directly into decision-making. As a research actor shared, "I’m not sure that I know quite what all those channels really look like…I’m sure that there’s a lot of improvement that could be driven in terms of how we bring strong evidence to actual policy and practice" (IDI14). Another actor explicitly named the way information flowed into decision-making in the province as "organic" (IDI7). They also noted the lack of a formal, independent science advisory panel for BC’s COVID-19 response, which existed in other provincial and international jurisdictions. Relatedly, one regional health authority actor perceived that the committee that was convened to advise the province on research, and established for the purpose of applying research to the COVID-19 response, "should have focused more on knowledge translation, but too much time was spent commissioning research and asking what kinds of questions we needed to ask rather than looking at what was happening in other jurisdictions" (IDI6). Overall, multiple actors noted a lack of clarity around application of evidence and who is responsible for ensuring evidence is applied. As a BCCDC actor expressed, in relation to how to prevent transmission of COVID-19:

We probably knew most of the things that we needed to know about May of last year [2020]. So, to me, it’s not even what evidence you need to know about, but who’s responsible for making sure that you actually apply the evidence to the intervention? Because so many of our interventions have been driven by peer pressure and public expectation rather than what we know to be the case [scientifically] (IDI1).

Some described the significance of predictive disease modelling to understand the COVID-19 trajectory and inform decisions, as well as to demonstrate to the public the effectiveness of particular measures, which "help[ed] sustain our response" (IDI2). Others, however, perceived that "mathematical models were vastly overused [and] overvalued in decision-making around this pandemic" (IDI1) and that modellers stepped outside their realm of expertise in providing models and policy recommendations through the public media.

Overall, while many actors’ impressions were that the response was evidence-informed, an overarching theme was a lack of clarity and uncertainty with respect to how evidence actually flowed into decision-making processes, as well as what specific evidence was used and how. Participants noted various mechanisms created or already in place prior to COVID-19 that fed data into, and facilitated, decision-making. There was an acknowledgement that multiple forms of evidence—including scientific data, data on public perceptions, as well as public pressure—appeared to have influenced decision-making.

Interface between researchers and policy-makers

There was a general sense that the Ministry supported the use of scientific and research-based evidence specifically. Some actors identified particular Ministry personnel as being especially amenable to research and focused on data to inform decisions and implementation. More broadly, the government-research interface was characterized by one actor as an amicable one, a "research-friendly government", and that the Ministry of Health (MOH), specifically, has a research strategy whereby, "it’s literally within their bureaucracy to become a more evidence-informed organization" (IDI11). The MOH was noted to have funded a research network intended to channel evidence into health policy and practice, and which reported to the research side of the MOH.

Other actors perceived relatively limited engagement with the broader scientific community. Some perceived an overreliance on 'in-house expertise' or a "we can do that [ourselves] mentality" within government that precluded academic researchers’ involvement, as well as a sense of "not really always wanting to engage with academics to answer policy questions because they don’t necessarily see the value that comes" (IDI14). With respect to the role of research, an actor stated:

There needs to be a provincial dialogue around what evidence is and how it gets situated, because there’s been some tension around evidence being produced and not used or at least not used in the way that researchers think that it should be (IDI11).

Those involved in data analytics within the MOH acknowledged a challenge in making epidemiological data available to academic researchers, because "at the time, you’re just trying to get decisions made" (IDI7). Relatedly, a research actor described the rapid instigation of COVID-19 research and pivoting of academic research programs to respond to the pandemic, but perceived a slow uptake of these research efforts from the MOH and PHSA for decision-making and action. Nevertheless, they too acknowledged the challenge of using research evidence, specifically, in an evolving and dynamic pandemic:

I think we’ve got to be realistic about what research in a pandemic situation can realistically contribute within very short timelines. I mean, some of these decisions have to be made very quickly...they were intuitive decisions, I think some of them, rather than necessarily evidence-based decisions (IDI14).

Relatedly, perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform governance during the pandemic. Largely from Ministry stakeholders, and those within the PHSA, the impressions were that Ministry actors were relying on public health advice and scientific expertise. As one actor articulated, "[the] government actually respected and acknowledged and supported public health expertise" (IDI9). Others emphasized a "trust of the people who understood the problem" (IDI3)—namely, those within public health—and perceived that public health experts were enabled "to take a lead role in the health system, over politics" (IDI12). This perspective was not as widely held by those in the public health sector, as one public health actor expressed, "politicians and bureaucrats waded into public health practice in a way that I don't think was appropriate" and that, "in the context of a pandemic, it’s actually relatively challenging to bring true expert advice because there’s too many right now. Suddenly, everybody’s a public health expert, but especially bureaucrats and politicians." They went on to share that the independence of public health to speak and act—and for politicians to accept independent public health advice—needs to be protected and institutionalized as "core to good governance" (IDI1). Relatedly, an elected official linked this to the absence of a formal, independent science table to advise government and stated that, "I think we should have one established permanently. I think we need to recognize that politicians aren't always the best at discerning scientific evidence and how that should play into decision-making" (IDI15).

These results highlight the divergent perspectives participants had as to the interface between research and policy-making and a lack of understanding regarding process and roles.

Challenges in applying evidence to policy decisions

Perspectives converged with respect to the existence of numerous challenges with and barriers to applying evidence to health policy and decision-making. These related to the quality and breadth of available data, both in terms of absence and abundance. For instance, as one public health actor noted in relation to health policy-making, "you never have enough information. You always have an information shortage, so you're trying to make the best decisions you can in the absence of usually really clear information" (IDI8). On the other hand, as evidence emerged en masse across jurisdictions in the pandemic, there were challenges with synthesizing evidence in a timely fashion for 'real-time' decision-making. A regional health authority actor highlighted this challenge early in the COVID-19 pandemic and perceived that there was not a provincial group bringing new synthesized information to decision-makers on a daily basis (IDI6). Other challenges related to the complexity of the political-public health interface with respect to data and scientific expertise, which "gets debated and needs to be digested by the political process. And then decisions are made" (IDI5). This actor further expressed that debate among experts needs to be balanced with efficient crisis response, that one has to "cut the debate short. For the sake of expediency, you need to react."

It was observed that, in BC’s COVID-19 response, data was gathered from multiple sources with differing data collection procedures, and sometimes with conflicting results—for instance, 'health system data' analyzed by the PHSA and 'public health data' analyzed by the BCCDC. This was observed to present challenges from a political perspective in discerning "who’s actually getting the 'right' answers" (IDI7). An added layer of complexity was reportedly rooted in how to communicate such evidence to the public and "public trust in the numbers" (IDI7), particularly as public understanding of what evidence is, how it is developed, and why it changes, can influence public perceptions of governance.

Finally, as one actor from within the research sector noted, organizationally and governance-wise, the system was "not very well set up to actually use research evidence…if we need to do better at using evidence in practice, we need to fix some of those things. And we actually know what a lot of those things are." For example , "there’s no science framework for how organizations work within that" and " governments shy away from setting science policy " (IDI11). This challenge was framed as having a macro-level dimension, as higher-level leadership structures were observed to not incentivize the development and effective use of research among constituent organizations, and also micro-level implications. From their perspective, researchers will struggle without such policy frameworks to obtain necessary data-sharing agreements with health authorities, nor will they be able to successfully navigate other barriers to conducting action-oriented research that informs policy and practice.

Similarly, a research actor perceived that the COVID-19 pandemic highlighted pre-existing fragmentation, "a pretty disjointed sort of enterprise" in how research is organized in the province:

I think pandemics need strong leadership and I think pandemic research response needed probably stronger leadership than it had. And I think that’s to do with [how] no one really knew who was in charge because no one really was given the role of being truly in charge of the research response (IDI14).

This individual underscored that, at the time of the interview, there were nearly 600 separate research projects being conducted in BC that focused on COVID-19. From their perspective, this reflected the need for more centralized direction to provide leadership, coordinate research efforts, and catalyze collaborations.

Overall, respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy.

This study explored the use of evidence to inform early COVID-19 decision-making within British Columbia, Canada, from the perspectives of decision-makers themselves. Findings underscore the complexity of synthesizing and applying evidence (i.e., ‘scientific’ or research-based evidence most commonly discussed) to support public health policy in 'real-time', particularly in the context of public health crisis response. Despite a substantial and long-established literature on evidence-based clinical decision-making [ 23 , 24 ], understanding is more limited as to how public health crisis decision-making can be evidence-informed or evidence-based. By contributing to a growing global scholarship of retrospective examinations of COVID-19 decision-making processes [ 25 , 26 , 27 , 28 ], our study aimed to broaden this understanding and, thus, support the strengthening of public health emergency preparedness in Canada, and globally.

Specifically, based on our findings on evidence-based public health practice, we found that decision-makers clearly emphasized ‘evidence-based’ or ‘evidence-informed’ as meaning ‘scientific’ evidence. They acknowledged other forms of evidence such as professional expertise and contextual information as influencing factors. We identified four key points related to the process of evidence-use in BC's COVID-19 decision-making, with broader implications as well:

Role Differences: The tensions we observed primarily related to a lack of clarity among the various agencies involved as to their respective roles and responsibilities in a public health emergency, a finding that aligns with research on evidence-use in prior pandemics in Canada [ 29 ]. Relatedly, scientists and policy-makers experienced challenges with communication and information-flow between one another and the public, which may reflect their different values and standards, framing of issues and goals, and language [ 30 ].

Barriers to Evidence-Use: Coordination and consistency in how data are collected across jurisdictions reportedly impeded efficiency and timeliness of decision-making. Lancaster and Rhodes (2020) suggest that evidence itself should be treated as a process, rather than a commodity, in evidence-based practice [ 31 ]. Thus, shifting the dialogue from 'barriers to evidence use' to an approach that fosters dialogue across different forms of evidence and different actors in the process may be beneficial.

Use of Evidence in Public Health versus Medicine: Evidence-based public health can be conflated with the concept of evidence-based medicine, though these are distinct in the type of information that needs to be considered. While ‘research evidence’ was the primary type of evidence used, other important types of evidence informed policy decisions in the COVID-19 public health emergency—for example, previous experience, public values, and preferences. This concurs with Brownson’s (2009) framework of factors driving decision-making in evidence-based public health [ 32 ]. Namely, that a balance between multiple factors, situated in particular environmental and organizational context, shapes decision-making: 1) best available research evidence; 2) clients'/population characteristics, state, needs, values, and preferences; and 3) resources, including a practitioner’s expertise. Thus, any evaluation of evidence-use in public health policy must take into consideration this multiplicity of factors at play, and draw on frameworks specific to public health [ 33 ]. Moreover, public health decision-making requires much more attention to behavioural factors and non-clinical impacts, which is distinct from the largely biology-focused lens of evidence-based medicine.

Transparency: Many participants emphasized a lack of explanation about why certain decisions were made and a lack of understanding about who was involved in decisions and how those decisions were made. This point was confirmed by a recent report on lessons learned in BC during the COVID-19 pandemic in which the authors describe " the desire to know more about the reasons why decisions were taken " as a " recurring theme " (13:66). These findings point to a need for clear and transparent mechanisms for channeling evidence, irrespective of the form used, into public health crisis decision-making.

Our findings also pointed to challenges associated with the infrastructure for utilizing research evidence in BC policy-making, specifically a need for more centralized authority on the research side of the public health emergency response to avoid duplication of efforts and more effectively synthesize findings for efficient use. Yet, as a participant questioned, what is the realistic role of research in a public health crisis response? Generally, most evidence used to inform crisis response measures is local epidemiological data or modelling data [ 7 ]. As corroborated by our findings, challenges exist in coordinating data collection and synthesis of these local data across jurisdictions to inform 'real-time' decision-making, let alone to feed into primary research studies [ 34 ].

On the other hand, as was the case in the COVID-19 pandemic, a 'high noise' research environment soon became another challenge as data became available to researchers. Various mechanisms have been established to try and address these challenges amid the COVID-19 pandemic, both to synthesize scientific evidence globally and to create channels for research evidence to support timely decision-making. For instance: 1) research networks and collaborations are working to coordinate research efforts (e.g., COVID-END network [ 35 ]); 2) independent research panels or committees within jurisdictions provide scientific advice to inform decision-making; and 3) research foundations, funding agencies, and platforms for knowledge mobilization (e.g., academic journals) continue to streamline funding through targeted calls for COVID-19 research grant proposals, or for publication of COVID-19 research articles. While our findings describe the varied forms of evidence used in COVID-19 policy-making—beyond scientific evidence—they also point to the opportunity for further investments in infrastructure that coordinates, streamlines, and strengthens collaborations between health researchers and decision-makers that results in timely uptake of results into policy decisions.

Finally, in considering these findings, it is important to note the study's scope and limitations: We focused on evidence use in a single public health emergency, in a single province. Future research could expand this inquiry to a multi-site analysis of evidence-use in pandemic policy-making, with an eye to synthesizing lessons learned and best practices. Additionally, our sample of participants included only one elected official, so perspectives were limited from this type of role. The majority of participants were health officials who primarily referred to and discussed evidence as ‘scientific’ or research-based evidence. Further work could explore the facilitators and barriers to evidence-use from the perspectives of elected officials and Ministry personnel, particularly with respect to the forms of evidence—considered broadly—and other varied inputs, that shape decision-making in the public sphere. This could include a more in-depth examination of policy implementation and how the potential societal consequences of implementation factor into public health decision-making.

We found that the policy decisions made during the initial stages of the COVID-19 pandemic were perceived by actors in BC's response as informed by—not always based on—scientific evidence, specifically; however, decision-makers also considered other contextual factors and drew on prior pandemic-related experience to inform decision-making, as is common in evidence-based public health practice [ 32 ]. The respondents' experiences point to specific areas that need to be considered in planning for future public health emergencies, including information flow between policy-makers and researchers, coordination in how data are collected, and transparency in how decisions are made—all of which reflect a need to improve communication. Furthermore, shifting the discourse from evidence as a commodity to evidence-use as a process will be helpful in addressing barriers to evidence-use, as well as increasing understanding about the public health decision-making process as distinct from clinical medicine. Finally, there is a critical need for clear mechanisms that channel evidence (whether ‘scientific’, research-based, or otherwise) into health crisis decision-making, including identifying and communicating the decision-making process to those producing and synthesizing evidence. The COVID-19 pandemic experience is an opportunity to reflect on what needs to be done to guild our public health systems for the future [ 36 , 37 ]. Understanding and responding to the complexities of decision-making as we move forward, particularly with respect to the synthesis and use of evidence, can contribute to strengthening preparedness for future public health emergencies.

Availability of data and materials

The data that support the findings of this study are not publicly available to maintain the confidentiality of research participants.

The terms 'evidence-informed' and 'evidence-based' decision-making are used throughout this paper, though are distinct. The term 'evidence-informed' suggests that evidence is used and considered, though not necessarily solely determinative in decision-making [ 38 ].

The Provincial Health Services Authority (PHSA) works with the Ministry of Health (MOH) and regional health authorities to oversee the coordination and delivery of programs.

The Office of the Provincial Health Officer (PHO) has binding legal authority in the case of an emergency, and responsibility to monitor the health of BC’s population and provide independent advice to Ministers and public offices on public health issues.

The British Columbia Centre for Disease Control (BCCDC) is a program of the PHSA and provides provincial and national disease surveillance, detection, treatment, prevention, and consultation.

Abbreviations

British Columbia

British Columbia Centre for Disease Control

Coronavirus Disease 2019

Medical Health Officer

Ministry of Health

Provincial Health Officer

Provincial Health Services Authority

Severe Acute Respiratory Syndrome Coronavirus—2

University of British Columbia

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Acknowledgements

We would like to extend our gratitude to current and former members of the University of British Columbia Working Group on Health Systems Response to COVID-19 who contributed to various aspects of this study, including Shelly Keidar, Kristina Jenei, Sydney Whiteford, Dr. Md Zabir Hasan, Dr. David M. Patrick, Dr. Maxwell Cameron, Mahrukh Zahid, Dr. Yoel Kornreich, Dr. Tammi Whelan, Austin Wu, Shivangi Khanna, and Candice Ruck.

Financial support for this work was generously provided by the University of British Columbia's Faculty of Medicine (Grant No. GR004683) and Peter Wall Institute for Advanced Studies (Grant No. GR016648), as well as a Canadian Institutes of Health Research Operating Grant (Grant No. GR019157). These funding bodies were not involved in the design of the study, the collection, analysis or interpretation of data, or in the writing of this manuscript.

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CYL, PB, and VS obtained funding for and designed the study. LJB, MC, and PB conducted data collection. LJB and VS analyzed the qualitative data. CYL and LJB collaboratively wrote the manuscript. All authors read and approved the final manuscript.

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Semi-structured interview guide [* = questions used for this specific study]

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Brubacher, L.J., Lovato, C.Y., Sriram, V. et al. The use of evidence to guide decision-making during the COVID-19 pandemic: divergent perspectives from a qualitative case study in British Columbia, Canada. Health Res Policy Sys 22 , 66 (2024). https://doi.org/10.1186/s12961-024-01146-2

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Matt Abrahams (Image credit: Courtesy Matt Abrahams)

How do you feel when a meeting has been canceled? Matt Abrahams believes nearly everybody is thrilled.

“Most people feel meetings are not as effective as they could be,” says Abrahams, a lecturer in organizational behavior at the Graduate School of Business and host of Think Fast Talk Smart: The Podcast . “However, it is possible to have well-run meetings that are productive, that you look forward to, and that good things come from.”

Effective meetings require thoughtful consideration beforehand of what you want to accomplish, he says, which also helps you determine whether a meeting is even necessary or if the goal could be achieved another way – such as via email or Slack.

“People often use meetings as Band-Aids for deeper communication issues. So when communication isn’t clear and consistent, people either put more meetings on the calendar, or more people show up for meetings because it’s the only avenue for direct communication.”

When you really do need to call a meeting, these six tips from Abrahams will help you make it worthwhile.

Send an inviting invitation

A good meeting invitation engages attendees from the beginning and sets the tone for successful collaboration, Abrahams says, and it all begins with the name of your meeting.

“Don’t put the word ‘meeting’ or any synonym for it in the title; rather, include an action specific to your purpose. For example, instead of ‘Update Meeting’ or ‘Process Improvement Summit,’ take a marketing mindset and have the meeting title be ‘App Launch’ or ‘Catalyzing Research Effectiveness.’ ”

In the description, briefly state the purpose of the meeting and link to the agenda, if there is one. “I also often include a task or question or challenge that I want people to work on prior,” he says. “And if we’re using tools such as Zoom, I put a link to a tutorial for those tools. It conveys that I care that everyone can be successful in the meeting.”

Be mindful of timing

Most of us tend to set meetings that suit our own schedules, without considering how refreshed, rushed, or tired the attendees may be, Abrahams says.

“It’s not about what’s convenient for you. It’s about what’s best for your participants so that they can be more productive,” Abrahams says. “If your participants had three meetings prior to yours, you may want to move your meeting to another, better time.”

Another trick? Match the length of the meeting to the tasks involved. “It’s OK to have a 22-minute meeting if that’s all the time you need. Some research shows that when you truncate the time of a meeting, people are more efficient. You needn’t just accept the 30- or 60-minute meeting times provided in most calendaring tools,” he says.

Set your agenda

If your meeting will cover more than one or two orders of business, Abrahams says, an agenda will help: List the items to be covered, who the item’s owner is, how long the item is expected to take, and whether the item is up for discussion, informational only, and/or requires action. And for best results, be strategic about the order.

“We often list items in the order that comes to mind or maybe the order of people’s seniority, but research suggests the complexity or the challenge involved should be considered,” he says.

“If the group already knows how to work with each other, start with the middle-intensity issue, then move to the most challenging, and end with the easiest. If people don’t know each other, starting with the easiest gives you a quick win and builds a sense of camaraderie.”

Open with action

Abrahams says he’s on a personal mission to change the way presentations and meetings start. “Most people start meetings by stating the purpose and reviewing what happened in the previous meeting. It’s ludicrous since we are often just reminding people of the previous meeting they did not enjoy!”

Better, he says, is to begin with an action, such as answering a question or doing a collaborative task. “I would much rather participants get engaged and involved in something, and then we can tell them what the meeting is about and review the previous meetings.”

Encourage participation

All meeting facilitators need to be concerned about contribution equity, Abrahams says, so it’s critical to help all participants feel comfortable sharing their input. “For example, in the midst of a virtual meeting where some folks have yet to participate, I may send you a chat and say, ‘I recall you shared some ideas on this topic in the past, might you want to share some now?’ ”

In hybrid meetings where some people are remote and some are in the room, Abrahams says, starting with whichever group contains fewer people when seeking input invites more equal contributions.

And, be sure to acknowledge those who contribute, either in the meeting, outside the meeting, or in chat. “For example, you could say, ‘That was really useful when you brought that point back up because we’d lost track of it.’ This acknowledgment encourages folks to share more.”

Rotate roles

For recurring meetings with the same group, Abrahams suggests having various members rotate through roles like facilitator and note taker. “This way, everyone comes to understand why it is important to pay attention and participate – along with how hard it is to run the meeting.”

Matt Abrahams is the author of the book Think Faster, Talk Smarter. Tune into the Think Fast Talk Smart podcast in late January for two back-to-back episodes on making meetings effective.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

Cite this Scribbr article

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

McCombes, S. (2022, October 25). How to Write a Discussion Section | Tips & Examples. Scribbr. Retrieved 3 June 2024, from https://www.scribbr.co.uk/thesis-dissertation/discussion/

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Check your sources —

Google’s “ai overview” can give false, misleading, and dangerous answers, from glue-on-pizza recipes to recommending "blinker fluid," google's ai sourcing needs work..

Kyle Orland - May 24, 2024 11:00 am UTC

Further Reading

Factual errors can pop up in existing LLM chatbots as well, of course. But the potential damage that can be caused by AI inaccuracy gets multiplied when those errors appear atop the ultra-valuable web real estate of the Google search results page.

"The examples we've seen are generally very uncommon queries and aren’t representative of most people’s experiences," a Google spokesperson told Ars. "The vast majority of AI Overviews provide high quality information, with links to dig deeper on the web."

After looking through dozens of examples of Google AI Overview mistakes (and replicating many ourselves for the galleries below), we've noticed a few broad categories of errors that seemed to show up again and again. Consider this a crash course in some of the current weak points of Google's AI Overviews and a look at areas of concern for the company to improve as the system continues to roll out.

Treating jokes as facts

• The bit about using glue on pizza can be traced back to an 11-year-old troll post on Reddit. ( via ) Kyle Orland / Google
• This wasn't funny when the guys at Pep Boys said it, either. ( via ) Kyle Orland / Google
• Weird Al recommends "running with scissors" as well! ( via ) Kyle Orland / Google

Some of the funniest example of Google's AI Overview failing come, ironically enough, when the system doesn't realize a source online was trying to be funny. An AI answer that suggested using "1/8 cup of non-toxic glue" to stop cheese from sliding off pizza can be traced back to someone who was obviously trying to troll an ongoing thread . A response recommending "blinker fluid" for a turn signal that doesn't make noise can similarly be traced back to a troll on the Good Sam advice forums , which Google's AI Overview apparently trusts as a reliable source.

In regular Google searches, these jokey posts from random Internet users probably wouldn't be among the first answers someone saw when clicking through a list of web links. But with AI Overviews, those trolls were integrated into the authoritative-sounding data summary presented right at the top of the results page.

What's more, there's nothing in the tiny "source link" boxes below Google's AI summary to suggest either of these forum trolls are anything other than good sources of information. Sometimes, though, glancing at the source can save you some grief, such as when you see a response calling running with scissors "cardio exercise that some say is effective" ( that came from a 2022 post from Little Old Lady Comedy ).

Bad sourcing

• Washington University in St. Louis says this ratio is accurate, but others disagree. ( via ) Kyle Orland / Google
• Man, we wish this fantasy remake was real. ( via ) Kyle Orland / Google

Sometimes Google's AI Overview offers an accurate summary of a non-joke source that happens to be wrong. When asking about how many Declaration of Independence signers owned slaves, for instance, Google's AI Overview accurately summarizes a Washington University of St. Louis library page saying that one-third "were personally enslavers." But the response ignores contradictory sources like a Chicago Sun-Times article saying the real answer is closer to three-quarters. I'm not enough of a history expert to judge which authoritative-seeming source is right, but at least one historian online took issue with the Google AI's answer sourcing .

Other times, a source that Google trusts as authoritative is really just fan fiction. That's the case for a response that imagined a 2022 remake of 2001: A Space Odyssey , directed by Steven Spielberg and produced by George Lucas. A savvy web user would probably do a double-take before citing citing Fandom's "Idea Wiki" as a reliable source, but a careless AI Overview user might not notice where the AI got its information.

reader comments

Promoted comments.

View attachment 81471

• garbage in, garbage out. Even the LLM says it's from a Reddit post.
• people having unrealistic expectations about LLMs. Perhaps this will convince everyone that they're parroting what they're fed and have no understanding or self consciousness.
• google shooting themselves in the foot. It's one thing to give a result like the Reddit suggesion as a link to the original post on Reddit. It's another one entirely to get it in this overview where it sounds like it's endorsed by Google.

Channel Ars Technica

GPT-4 is better than humans at financial forecasting, new study shows

• OpenAI's GPT-4 is better than humans at analyzing financial statements and making forecasts, according to a new study.
• "Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes," the study found.
• Trading strategies based on GPT-4 also delivered more profitable results than the stock market.

OpenAI's GPT-4 proved to be a better financial analyst than humans, according to a new study.

The findings could upend the financial services industry that, like other business sectors, is racing to adopt generative AI technologies.

According to the study conducted by the Booth School of Business at the University of Chicago, the large language model did a better job of analyzing financial statements and making predictions based on those statements.

"Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes," the study said. "The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle."

The study utilized "chain-of-thought" prompts that directed GPT-4 to identify trends in financial statements and calculate different financial ratios. From there, the large language model analyzed the information and predicted future earnings results.

"When we use the chain of thought prompt to emulate human reasoning, we find that GPT achieves an accuracy of 60%, which is remarkably higher than that achieved by the analysts," the study said. The human analysts were closer to the low 50% range with regard to prediction accuracy.

The large language models' ability to recognize financial patterns and business concepts with incomplete information suggests that the technology should play a key role in financial decision-making going forward, according to the study's authors.

Finally, the study found that applying GPT-4's financial acumen to trading strategies produced more profitable trading, with higher share ratios and alpha that ultimately beat the stock market.

"We find that the long-short strategy based on GPT forecasts outperforms the market and generates significant alphas and Sharpe ratios," the study said. 

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Low socioeconomic status raises pregnant women's risk of exposure to thyroid-disrupting chemicals, study finds

by The Endocrine Society

Exposure to some endocrine-disrupting chemicals (EDCs) that harm the thyroid gland has increased over the past 20 years among U.S. women of childbearing age and pregnant women, especially among those with lower social and economic status, a new study finds. The results are presented at ENDO 2024 , the Endocrine Society's annual meeting in Boston, Mass.

"Our research underscores the importance of addressing socioeconomic disparities in EDC exposure among women of reproductive age and pregnant women to mitigate potential adverse effects on thyroid health," said senior author Elizabeth N. Pearce, M.D., M.Sc., of the Boston University Chobanian & Avedisian School of Medicine in Boston.

EDCs are common substances in the environment, foods and manufactured products that interfere with the body's hormones and harm public health. EDCs can affect thyroid hormones , which control many functions of the body and are important for the brain development of fetuses and infants.

The researchers focused this study on women who may be particularly vulnerable to negative effects of EDCs on the thyroid: women in their childbearing years and pregnant women.

Researcher Cheng Han, M.D., of the Boston University Chobanian & Avedisian School of Medicine analyzed data from the U.S. National Health and Nutrition Survey (NHANES) from 1999 to 2020 for 25,320 reproductive-age women and 2,525 pregnant women. He assessed trends over the past two decades in levels of multiple thyroid-disrupting chemicals in blood and urine samples. Statistical tests helped him to evaluate changes in EDC exposure over time and to identify the effect of socioeconomic status on this exposure.

Han found that exposure to many of the EDCs decreased for both groups of women over the 20-year study period. However, exposure to some thyroid-disrupting chemicals increased in that period. Both reproductive-age women and pregnant women had increased exposure to two types of polyaromatic hydrocarbons.

Common sources of exposure to these chemicals include breathing cigarette smoke , wood smoke or motor vehicle exhaust or eating grilled foods, according to the U.S. Centers for Disease Control and Prevention (CDC).

Han said that low-income women who were pregnant or of reproductive age had the greatest increase in exposure to thyroid-disrupting chemicals, especially polyaromatic hydrocarbons.

"This increased exposure has the potential to worsen disparities in health outcomes among low-income people," he said.

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