what is significant findings in research

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Statistical significance.

Steven Tenny ; Ibrahim Abdelgawad .

Affiliations

Last Update: November 23, 2023 .

• Introduction

In research, statistical significance measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer. We can better understand statistical significance if we break apart a study design. [1] [2] [3] [4] [5] [6] [7]

When creating a study, the researcher has to start with a hypothesis; that is, they must have some idea of what they think the outcome may be. For example, a study is researching a new medication to lower blood pressure. The researcher hypothesizes that the new medication lowers systolic blood pressure by at least 10 mm Hg compared to not taking the new medication. The hypothesis can be stated: "Taking the new medication will lower systolic blood pressure by at least 10 mm Hg compared to not taking the medication." In science, researchers can never prove any statement as there are infinite alternatives as to why the outcome may have occurred. They can only try to disprove a specific hypothesis. The researcher must then formulate a question they can disprove while concluding that the new medication lowers systolic blood pressure. The hypothesis to be disproven is the null hypothesis and typically the inverse statement of the hypothesis. Thus, the null hypothesis for our researcher would be, "Taking the new medication will not lower systolic blood pressure by at least 10 mm Hg compared to not taking the new medication." The researcher now has the null hypothesis for the research and must specify the significance level or level of acceptable uncertainty.

Even when disproving a hypothesis, the researcher can not be 100% certain of the outcome. The researcher must then settle for some level of confidence, or the degree of significance, for which they want to be confident their finding is correct. The significance level is given the Greek letter alpha and specified as the probability the researcher is willing to be incorrect. Generally, a researcher wants to be correct about their outcome 95% of the time, so the researcher is willing to be incorrect 5% of the time. Probabilities are decimals, with 1.0 being entirely positive (100%) and 0 being completely negative (0%). Thus, the researcher who wants to be 95% sure about the outcome of their study is willing to be wrong about the result 5% of the time. The alpha is the decimal expression of how much they are ready to be incorrect. For the current example, the alpha is 0.05. The level of uncertainty the researcher is willing to accept (alpha or significance level) is 0.05, or a 5% chance they are incorrect about the study's outcome.

Now, the researcher can perform the research. In this example, a prospective randomized controlled study is conducted in which the researcher gives some individuals the new medication and others a placebo. The researcher then evaluates the blood pressure of both groups after a specified time and performs a statistical analysis of the results to obtain a  P  value (probability value). Several different tests can be performed depending on the type of variable being studied and the number of subjects. The exact test is outside the scope of this review, but the output would be a  P  value. Using the correct statistical analysis tool when calculating the  P  value is imperative. If the researchers use the wrong test, the  P  value will not be accurate, and this result can mislead the researcher. A  P  value is a probability under a specified statistical model that a statistical summary of the data (eg, the sample mean difference between 2 compared groups) would be equal to or more extreme than its observed value.

In this example, the researcher hypothetically found blood pressure tended to decrease after taking the new medication, with an average decrease of 15 mm Hg in the group taking the new medication. The researcher then used the help of their statistician to perform the correct analysis and arrived at a  P  value of 0.02 for a decrease in blood pressure in those taking the new medication versus those not taking the new medication. This researcher now has the 3 required pieces of information to look at statistical significance: the null hypothesis, the significance level, and the  P  value.

The researcher can finally assess the statistical significance of the new medication. A study result is statistically significant if the  P  value of the data analysis is less than the prespecified alpha (significance level). In this example, the P value is 0.02, which is less than the prespecified alpha of 0.05, so the researcher rejects the null hypothesis, which has been determined within the predetermined confidence level to be disproven, and accepts the hypothesis, thus concluding there is statistical significance for the finding that the new medication lowers blood pressure. 

What does this mean? The P value is not the probability of the null hypothesis itself. It is the probability that, if the study were repeated an infinite number of times, one would expect the findings to be as, or more extreme, than the one calculated in this test. Therefore, the  P  value of 0.02 would signify that 2% of the infinite tests would find a result at least as extreme as the one in this study. Given that the null hypothesis states that there is no significant change in blood pressure if the patient is or is not taking the new medication, we can assume that this statement is false, as 98% of the infinite studies would find that there was indeed a reduction in blood pressure. However, as the  P  value implies, there is a chance that this is false, and there truly is no effect of the medication on the blood pressure. However, as the researcher prespecified an acceptable confidence level with an alpha of 0.05, and the  P  value is 0.02, less than the acceptable alpha of 0.05, the researcher rejects the null hypothesis. By rejecting the null hypothesis, the researcher accepts the alternative hypothesis. The researcher rejects the idea that there is no difference in systolic blood pressure with the new medication and accepts a difference of at least 10 mm Hg in systolic blood pressure when taking the new medication.

If the researcher had prespecified an alpha of 0.01, implying they wanted to be 99% sure the new medication lowered the blood pressure by at least 10 mm Hg, the  P  value of 0.02 would be more significant than the prespecified alpha of 0.01. The researcher would conclude the study did not reach statistical significance as the  P  value is equal to or greater than the prespecified alpha. The research would then not be able to reject the null hypothesis.

A study is statistically significant if the  P  value is less than the pre-specified alpha. Stated succinctly:

• A  P  value less than a predetermined alpha is considered a statistically significant result 
• A  P  value greater than or equal to alpha is not a statistically significant result.
• Issues of Concern

A few issues of concern when looking at statistical significance are evident. These issues include choosing the alpha, statistical analysis method, and clinical significance.

Many current research articles specify an alpha of 0.05 for their significance level. It cannot be stated strongly enough that there is nothing special, mathematical, or certain about picking an alpha of 0.05. Historically, the originators concluded that for many applications, an alpha of 0.05, or a one in 20 chance of being incorrect, was good enough. The researcher must consider what the confidence level should genuinely be for the research question being asked. A smaller alpha, say 0.01, may be more appropriate.

When creating a study, the alpha, or confidence level, should be specified before any intervention or collection of data. It is easy for a researcher to "see what the data shows" and then pick an alpha to give a statistically significant result. Such approaches compromise the data and results as the researcher is more likely to be lax on confidence level selection to obtain a result that looks statistically significant.

A second important issue is selecting the correct statistical analysis method. There are numerous methods for obtaining a  P  value. The method chosen depends on the type of data, the number of data points, and the question being asked. It is essential to consider these questions during the study design so the statistical analysis can be correctly identified before the research. The statistical analysis method can help determine how to collect the data correctly and the number of data points needed. If the wrong statistical method is used, the results may be meaningless, as an incorrect  P  value would be calculated.

• Clinical Significance

A key distinction between statistical significance and clinical significance is evident. Statistical significance determines if there is mathematical significance to the analysis of the results. Clinical significance means the difference is vital to the patient and the clinician. This study's statistical significance would be present as the  P  value was less than the prespecified alpha. The clinical significance would be the 10 mmHg drop in systolic blood pressure. [6]

Two studies can have a similar statistical significance but vastly differ in clinical significance. In a hypothetical example of 2 new chemotherapy agents for treating cancer, Drug A increased survival by at least 10 years with a  P  value of 0.01 and an alpha for the study of 0.05. Thus, this study has statistical significance ( P  value less than alpha) and clinical significance (increased survival by 10 years). A second chemotherapy agent, Drug B, increases survival by at least 10 minutes with a  P  value of 0.01 and alpha for the study of 0.05. The study for Drug B also found statistical significance ( P  value less than alpha) but no clinical significance (a 10-minute increase in life expectancy is not clinically significant). In a separate study, those taking Drug A lived an average of 8 years after starting the medication versus living for only 2 more years for those not taking Drug A, with a  P  value of 0.08 and alpha for this second study of Drug A of 0.05. In this second study of Drug A, there is no statistical significance ( P  value greater than or equal to alpha).

• Enhancing Healthcare Team Outcomes

Each healthcare team member needs a basic understanding of statistical significance. All members of the care continuum, including nurses, physicians, advanced practitioners, social workers, and pharmacists, peruse copious literature and consider conclusions based on statistical significance. Suppose team members do not have a cohesive and harmonious understanding of the statistical significance and its implications for research studies and findings. In that case, various members may draw opposing conclusions from the same research.

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Ibrahim Abdelgawad declares no relevant financial relationships with ineligible companies.

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

• Cite this Page Tenny S, Abdelgawad I. Statistical Significance. [Updated 2023 Nov 23]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Research Findings Guide: Examples, Types, and Structuring Tips

• November 7, 2024

Dr. Marvin L. Smith

Research findings are the core insights derived from a study, summarizing key results and answering the research question . They reveal patterns, relationships, or trends, whether through qualitative insights or quantitative data. 

Understanding how to write findings in research is crucial—it provides clarity, supports claims, and often determines the study’s impact. 

This article explores types of research findings , examples, and methods to present them effectively. 

Whether you’re looking to learn about research findings, explore examples of different types of research findings, or need guidance on structuring findings in a paper, this guide has you covered.

What Are Research Findings?

Research findings are the key results or discoveries from a study. 

They directly address the research question, revealing insights that support or challenge the hypothesis. These findings can be qualitative, like observations or themes, or quantitative, like statistics or patterns. 

Clear and accurate findings ensure readers understand the study’s outcome.

Importance of Research Findings

Research findings are the cornerstone of any study, offering critical evidence to support the researcher’s conclusions . They serve as the basis for establishing facts, verifying hypotheses, and validating the study’s objectives. 

Findings not only demonstrate that a study has met its intended goals but also underscore its relevance and reliability within a field.

In academic and professional circles, strong research findings enhance the credibility of a paper. They demonstrate that the study is grounded in rigorous data analysis, increasing the likelihood of acceptance by peers and recognition in the wider community. 

When findings are presented clearly and backed by sound evidence, they provide a solid foundation for future research, inspiring new questions and guiding subsequent studies.

Additionally, well-structured findings are invaluable for decision-making across sectors. 

In healthcare , they inform treatment protocols and health policies; in business , they shape product development and strategic planning; in education , they enhance teaching methods and learning outcomes. 

Without concrete findings, research would lack direction and impact, making these insights essential for applying knowledge to real-world problems and advancing knowledge in meaningful ways.

Types of Research Findings

Research findings can be categorized based on both the data’s nature and its origin, giving readers insight into the study’s methods and the type of evidence presented. 

This classification—into qualitative vs. quantitative findings and primary vs. secondary findings—helps researchers structure their findings more effectively and ensures readers can follow the study’s approach.

Qualitative vs. Quantitative Findings

Qualitative findings focus on understanding experiences, motivations, and perceptions by capturing themes, patterns, and meanings through methods like interviews, focus groups, and observations. They address the “how” and “why” behind phenomena.

For instance, in a study exploring customer satisfaction, qualitative findings might reveal that customers feel valued when employees remember their names—an insight drawn from direct interview responses. 

These findings provide rich, contextual insights that add depth and human perspectives.

Quantitative findings , on the other hand, are based on numerical data derived from methods like surveys, experiments, and statistical analysis. These findings answer “what,” “how much,” or “how many,” offering a measurable view of trends or relationships.

In the same customer satisfaction study, quantitative findings could show that 78% of surveyed customers rate their satisfaction as “high.” 

This data-driven approach offers clear, objective metrics that validate or challenge hypotheses and allow comparisons across variables.

Using both qualitative and quantitative findings often provides a balanced perspective, combining numerical rigor with contextual understanding—a method known as mixed-methods research.

Primary vs. Secondary Findings

Primary findings emerge directly from the researcher’s own data collection. These are original insights obtained through firsthand research, such as an experiment, survey, or field study. 

For example, a study measuring the effects of a new medication on blood pressure would yield primary findings about its effectiveness based on the data collected during clinical trials. 

These findings introduce new knowledge to the field, making them highly valuable and directly tied to the study’s objectives.

Secondary findings are drawn from data or insights that others have previously collected. They often support or add context to primary findings without introducing new information. 

For instance, in a study on the effectiveness of teaching methods, secondary findings might include statistics from government reports on educational outcomes. 

These findings help frame the research within a broader context, showing how it aligns with or diverges from existing studies. By combining primary and secondary findings, researchers can enhance the credibility of their work and provide a fuller understanding of the topic.

Each type of research finding serves a unique purpose. 

Qualitative and quantitative findings provide different perspectives on data, while primary and secondary findings strengthen the depth and breadth of research, making it more impactful and informative.

Interpreting Research Findings

Interpreting research findings involves reviewing data to uncover meaningful insights. This process not only highlights key results but also strengthens the study’s credibility by ensuring clarity and accuracy in presenting findings.

Analyzing Data and Recognizing Patterns

Data analysis helps identify trends, correlations, or differences within the dataset. By recognizing these patterns, researchers draw conclusions that directly address the research question. Effective analysis reveals underlying insights and shows how findings connect to the study’s objectives.

Ensuring Validity and Accuracy

Ensuring validity and accuracy is essential in interpreting findings. Validity confirms that the findings genuinely reflect the data and align with the research question, while accuracy ensures consistent, error-free analysis. Together, they reinforce the study’s reliability, making its conclusions trustworthy and impactful.

Presenting Research Findings

Presenting research findings effectively is crucial for helping readers understand and engage with the study’s outcomes. A well-structured presentation and the use of visuals ensure clarity, while accessible language makes findings understandable to a wider audience.

Structuring a Clear Presentation

Organize findings in a logical order that directly addresses the research question, starting with the most significant results. Use headings, subheadings, and bullet points to break down information, making it easier for readers to follow. Concise and clear language keeps the focus on key insights without overwhelming details.

Using Visuals for Emphasis

Visuals, like charts, graphs, and tables, highlight key data points and make complex information easier to grasp.

For example, a bar chart can show survey results by comparing response percentages across different groups, while a line graph can track changes over time, such as monthly sales trends or patient recovery rates. 

Tables are also effective for presenting detailed numerical data, allowing readers to compare figures side by side.

These visual aids help readers quickly identify patterns and comparisons, enhancing the impact of findings and overall comprehension. A well-placed chart or table can make a difference by translating raw data into a clear, engaging visual summary .

Communicating Findings to Non-Experts

To reach non-experts, simplify technical terms and avoid jargon. Use clear, everyday language and provide brief explanations when needed. Presenting findings in an accessible way ensures broader understanding and maximizes the research’s reach and influence.

Challenges in Reporting Research Findings

Reporting research findings can be challenging, as it requires accuracy and objectivity to avoid misleading readers. Identifying and addressing these challenges is essential to maintain credibility and transparency.

Misinterpretation and Bias

Misinterpretation happens when findings are presented in a way that leads readers to incorrect conclusions. To avoid this, use precise language and clarify key points. Bias, whether intentional or unintentional, can distort findings by emphasizing certain outcomes. Being aware of potential biases and reporting objectively ensures a fair representation of the data.

Addressing Limitations

Every study has limitations—factors that may affect the results or the generalizability of findings. Clearly acknowledging these limitations shows honesty and helps readers understand the scope of the research. Addressing limitations also guides future studies by highlighting areas for improvement or further investigation.

Applications of Research Findings

Research findings have broad applications across various fields, guiding decisions, influencing policies, and informing future research. 

In healthcare, findings can lead to new treatments, improve patient care, or shape public health guidelines. 

In business, research insights drive product development, marketing strategies, and customer experience enhancements. 

In education, findings inform teaching methods and curriculum design, ultimately improving learning outcomes.

Moreover, research findings often serve as a foundation for further studies, allowing other researchers to build on existing knowledge. Whether applied to solve real-world problems or deepen understanding within a field, these findings contribute significantly to progress and innovation.

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What is the Significance of a Study? Examples and Guide

If you’re reading this post you’re probably wondering: what is the significance of a study?

No matter where you’re at with a piece of research, it is a good idea to think about the potential significance of your work. And sometimes you’ll have to explicitly write a statement of significance in your papers, it addition to it forming part of your thesis.

In this post I’ll cover what the significance of a study is, how to measure it, how to describe it with examples and add in some of my own experiences having now worked in research for over nine years.

If you’re reading this because you’re writing up your first paper, welcome! You may also like my how-to guide for all aspects of writing your first research paper .

Looking for guidance on writing the statement of significance for a paper or thesis? Click here to skip straight to that section.

What is the Significance of a Study?

For research papers, theses or dissertations it’s common to explicitly write a section describing the significance of the study. We’ll come onto what to include in that section in just a moment.

However the significance of a study can actually refer to several different things.

Working our way from the most technical to the broadest, depending on the context, the significance of a study may refer to:

• Within your study: Statistical significance. Can we trust the findings?
• Wider research field: Research significance. How does your study progress the field?
• Commercial / economic significance: Could there be business opportunities for your findings?
• Societal significance: What impact could your study have on the wider society.
• And probably other domain-specific significance!

We’ll shortly cover each of them in turn, including how they’re measured and some examples for each type of study significance.

But first, let’s touch on why you should consider the significance of your research at an early stage.

Why Care About the Significance of a Study?

No matter what is motivating you to carry out your research, it is sensible to think about the potential significance of your work. In the broadest sense this asks, how does the study contribute to the world?

After all, for many people research is only worth doing if it will result in some expected significance. For the vast majority of us our studies won’t be significant enough to reach the evening news, but most studies will help to enhance knowledge in a particular field and when research has at least some significance it makes for a far more fulfilling longterm pursuit.

Furthermore, a lot of us are carrying out research funded by the public. It therefore makes sense to keep an eye on what benefits the work could bring to the wider community.

Often in research you’ll come to a crossroads where you must decide which path of research to pursue. Thinking about the potential benefits of a strand of research can be useful for deciding how to spend your time, money and resources.

It’s worth noting though, that not all research activities have to work towards obvious significance. This is especially true while you’re a PhD student, where you’re figuring out what you enjoy and may simply be looking for an opportunity to learn a new skill.

However, if you’re trying to decide between two potential projects, it can be useful to weigh up the potential significance of each.

Let’s now dive into the different types of significance, starting with research significance.

Research Significance

What is the research significance of a study.

Unless someone specifies which type of significance they’re referring to, it is fair to assume that they want to know about the research significance of your study.

Research significance describes how your work has contributed to the field, how it could inform future studies and progress research.

Where should I write about my study’s significance in my thesis?

Typically you should write about your study’s significance in the Introduction and Conclusions sections of your thesis.

It’s important to mention it in the Introduction so that the relevance of your work and the potential impact and benefits it could have on the field are immediately apparent. Explaining why your work matters will help to engage readers (and examiners!) early on.

It’s also a good idea to detail the study’s significance in your Conclusions section. This adds weight to your findings and helps explain what your study contributes to the field.

On occasion you may also choose to include a brief description in your Abstract.

What is expected when submitting an article to a journal

It is common for journals to request a statement of significance, although this can sometimes be called other things such as:

• Impact statement
• Significance statement
• Advances in knowledge section

Here is one such example of what is expected:

Impact Statement:  An Impact Statement is required for all submissions.  Your impact statement will be evaluated by the Editor-in-Chief, Global Editors, and appropriate Associate Editor. For your manuscript to receive full review, the editors must be convinced that it is an important advance in for the field. The Impact Statement is not a restating of the abstract. It should address the following: Why is the work submitted important to the field? How does the work submitted advance the field? What new information does this work impart to the field? How does this new information impact the field? Experimental Biology and Medicine journal, author guidelines

Typically the impact statement will be shorter than the Abstract, around 150 words.

Defining the study’s significance is helpful not just for the impact statement (if the journal asks for one) but also for building a more compelling argument throughout your submission. For instance, usually you’ll start the Discussion section of a paper by highlighting the research significance of your work. You’ll also include a short description in your Abstract too.

How to describe the research significance of a study, with examples

Whether you’re writing a thesis or a journal article, the approach to writing about the significance of a study are broadly the same.

I’d therefore suggest using the questions above as a starting point to base your statements on.

• Why is the work submitted important to the field?
• How does the work submitted advance the field?
• What new information does this work impart to the field?
• How does this new information impact the field?

Answer those questions and you’ll have a much clearer idea of the research significance of your work.

When describing it, try to clearly state what is novel about your study’s contribution to the literature. Then go on to discuss what impact it could have on progressing the field along with recommendations for future work.

Potential sentence starters

If you’re not sure where to start, why not set a 10 minute timer and have a go at trying to finish a few of the following sentences. Not sure on what to put? Have a chat to your supervisor or lab mates and they may be able to suggest some ideas.

• This study is important to the field because…
• These findings advance the field by…
• Our results highlight the importance of…
• Our discoveries impact the field by…

Now you’ve had a go let’s have a look at some real life examples.

Statement of significance examples

A statement of significance / impact:

Impact Statement This review highlights the historical development of the concept of “ideal protein” that began in the 1950s and 1980s for poultry and swine diets, respectively, and the major conceptual deficiencies of the long-standing concept of “ideal protein” in animal nutrition based on recent advances in amino acid (AA) metabolism and functions. Nutritionists should move beyond the “ideal protein” concept to consider optimum ratios and amounts of all proteinogenic AAs in animal foods and, in the case of carnivores, also taurine. This will help formulate effective low-protein diets for livestock, poultry, and fish, while sustaining global animal production. Because they are not only species of agricultural importance, but also useful models to study the biology and diseases of humans as well as companion (e.g. dogs and cats), zoo, and extinct animals in the world, our work applies to a more general readership than the nutritionists and producers of farm animals. Wu G, Li P. The “ideal protein” concept is not ideal in animal nutrition.  Experimental Biology and Medicine . 2022;247(13):1191-1201. doi: 10.1177/15353702221082658

And the same type of section but this time called “Advances in knowledge”:

Advances in knowledge: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders. Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change. Size changes of at least 6 mm or more in  T 1  weighted or  T 2  weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change. Wennmann M, Grözinger M, Weru V, et al. Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma [published online ahead of print, 2023 Apr 12].  Br J Radiol . 2023;20220745. doi: 10.1259/bjr.20220745

Other examples of research significance

Moving beyond the formal statement of significance, here is how you can describe research significance more broadly within your paper.

Describing research impact in an Abstract of a paper:

Three-dimensional visualisation and quantification of the chondrocyte population within articular cartilage can be achieved across a field of view of several millimetres using laboratory-based micro-CT. The ability to map chondrocytes in 3D opens possibilities for research in fields from skeletal development through to medical device design and treatment of cartilage degeneration. Conclusions section of the abstract in my first paper .

In the Discussion section of a paper:

We report for the utility of a standard laboratory micro-CT scanner to visualise and quantify features of the chondrocyte population within intact articular cartilage in 3D. This study represents a complimentary addition to the growing body of evidence supporting the non-destructive imaging of the constituents of articular cartilage. This offers researchers the opportunity to image chondrocyte distributions in 3D without specialised synchrotron equipment, enabling investigations such as chondrocyte morphology across grades of cartilage damage, 3D strain mapping techniques such as digital volume correlation to evaluate mechanical properties  in situ , and models for 3D finite element analysis  in silico  simulations. This enables an objective quantification of chondrocyte distribution and morphology in three dimensions allowing greater insight for investigations into studies of cartilage development, degeneration and repair. One such application of our method, is as a means to provide a 3D pattern in the cartilage which, when combined with digital volume correlation, could determine 3D strain gradient measurements enabling potential treatment and repair of cartilage degeneration. Moreover, the method proposed here will allow evaluation of cartilage implanted with tissue engineered scaffolds designed to promote chondral repair, providing valuable insight into the induced regenerative process. The Discussion section of the paper is laced with references to research significance.

How is longer term research significance measured?

Looking beyond writing impact statements within papers, sometimes you’ll want to quantify the long term research significance of your work. For instance when applying for jobs.

The most obvious measure of a study’s long term research significance is the number of citations it receives from future publications. The thinking is that a study which receives more citations will have had more research impact, and therefore significance , than a study which received less citations. Citations can give a broad indication of how useful the work is to other researchers but citations aren’t really a good measure of significance.

Bear in mind that us researchers can be lazy folks and sometimes are simply looking to cite the first paper which backs up one of our claims. You can find studies which receive a lot of citations simply for packaging up the obvious in a form which can be easily found and referenced, for instance by having a catchy or optimised title.

Likewise, research activity varies wildly between fields. Therefore a certain study may have had a big impact on a particular field but receive a modest number of citations, simply because not many other researchers are working in the field.

Nevertheless, citations are a standard measure of significance and for better or worse it remains impressive for someone to be the first author of a publication receiving lots of citations.

Other measures for the research significance of a study include:

• Accolades: best paper awards at conferences, thesis awards, “most downloaded” titles for articles, press coverage.
• How much follow-on research the study creates. For instance, part of my PhD involved a novel material initially developed by another PhD student in the lab. That PhD student’s research had unlocked lots of potential new studies and now lots of people in the group were using the same material and developing it for different applications. The initial study may not receive a high number of citations yet long term it generated a lot of research activity.

That covers research significance, but you’ll often want to consider other types of significance for your study and we’ll cover those next.

Statistical Significance

What is the statistical significance of a study.

Often as part of a study you’ll carry out statistical tests and then state the statistical significance of your findings: think p-values eg <0.05. It is useful to describe the outcome of these tests within your report or paper, to give a measure of statistical significance.

Effectively you are trying to show whether the performance of your innovation is actually better than a control or baseline and not just chance. Statistical significance deserves a whole other post so I won’t go into a huge amount of depth here.

Things that make publication in  The BMJ  impossible or unlikely Internal validity/robustness of the study • It had insufficient statistical power, making interpretation difficult; • Lack of statistical power; The British Medical Journal’s guide for authors

Calculating statistical significance isn’t always necessary (or valid) for a study, such as if you have a very small number of samples, but it is a very common requirement for scientific articles.

Writing a journal article? Check the journal’s guide for authors to see what they expect. Generally if you have approximately five or more samples or replicates it makes sense to start thinking about statistical tests. Speak to your supervisor and lab mates for advice, and look at other published articles in your field.

How is statistical significance measured?

Statistical significance is quantified using p-values . Depending on your study design you’ll choose different statistical tests to compute the p-value.

A p-value of 0.05 is a common threshold value. The 0.05 means that there is a 1/20 chance that the difference in performance you’re reporting is just down to random chance.

• p-values above 0.05 mean that the result isn’t statistically significant enough to be trusted: it is too likely that the effect you’re showing is just luck.
• p-values less than or equal to 0.05 mean that the result is statistically significant. In other words: unlikely to just be chance, which is usually considered a good outcome.

Low p-values (eg p = 0.001) mean that it is highly unlikely to be random chance (1/1000 in the case of p = 0.001), therefore more statistically significant.

It is important to clarify that, although low p-values mean that your findings are statistically significant, it doesn’t automatically mean that the result is scientifically important. More on that in the next section on research significance.

How to describe the statistical significance of your study, with examples

In the first paper from my PhD I ran some statistical tests to see if different staining techniques (basically dyes) increased how well you could see cells in cow tissue using micro-CT scanning (a 3D imaging technique).

In your methods section you should mention the statistical tests you conducted and then in the results you will have statements such as:

Between mediums for the two scan protocols C/N [contrast to noise ratio] was greater for EtOH than the PBS in both scanning methods (both  p  < 0.0001) with mean differences of 1.243 (95% CI [confidence interval] 0.709 to 1.778) for absorption contrast and 6.231 (95% CI 5.772 to 6.690) for propagation contrast. … Two repeat propagation scans were taken of samples from the PTA-stained groups. No difference in mean C/N was found with either medium: PBS had a mean difference of 0.058 ( p  = 0.852, 95% CI -0.560 to 0.676), EtOH had a mean difference of 1.183 ( p  = 0.112, 95% CI 0.281 to 2.648). From the Results section of my first paper, available here . Square brackets added for this post to aid clarity.

From this text the reader can infer from the first paragraph that there was a statistically significant difference in using EtOH compared to PBS (really small p-value of <0.0001). However, from the second paragraph, the difference between two repeat scans was statistically insignificant for both PBS (p = 0.852) and EtOH (p = 0.112).

By conducting these statistical tests you have then earned your right to make bold statements, such as these from the discussion section:

Propagation phase-contrast increases the contrast of individual chondrocytes [cartilage cells] compared to using absorption contrast. From the Discussion section from the same paper.

Without statistical tests you have no evidence that your results are not just down to random chance.

Beyond describing the statistical significance of a study in the main body text of your work, you can also show it in your figures.

In figures such as bar charts you’ll often see asterisks to represent statistical significance, and “n.s.” to show differences between groups which are not statistically significant. Here is one such figure, with some subplots, from the same paper:

In this example an asterisk (*) between two bars represents p < 0.05. Two asterisks (**) represents p < 0.001 and three asterisks (***) represents p < 0.0001. This should always be stated in the caption of your figure since the values that each asterisk refers to can vary.

Now that we know if a study is showing statistically and research significance, let’s zoom out a little and consider the potential for commercial significance.

Commercial and Industrial Significance

What are commercial and industrial significance.

Moving beyond significance in relation to academia, your research may also have commercial or economic significance.

Simply put:

• Commercial significance: could the research be commercialised as a product or service? Perhaps the underlying technology described in your study could be licensed to a company or you could even start your own business using it.
• Industrial significance: more widely than just providing a product which could be sold, does your research provide insights which may affect a whole industry? Such as: revealing insights or issues with current practices, performance gains you don’t want to commercialise (e.g. solar power efficiency), providing suggested frameworks or improvements which could be employed industry-wide.

I’ve grouped these two together because there can certainly be overlap. For instance, perhaps your new technology could be commercialised whilst providing wider improvements for the whole industry.

Commercial and industrial significance are not relevant to most studies, so only write about it if you and your supervisor can think of reasonable routes to your work having an impact in these ways.

How are commercial and industrial significance measured?

Unlike statistical and research significances, the measures of commercial and industrial significance can be much more broad.

Here are some potential measures of significance:

Commercial significance:

• How much value does your technology bring to potential customers or users?
• How big is the potential market and how much revenue could the product potentially generate?
• Is the intellectual property protectable? i.e. patentable, or if not could the novelty be protected with trade secrets: if so publish your method with caution!
• If commercialised, could the product bring employment to a geographical area?

Industrial significance:

What impact could it have on the industry? For instance if you’re revealing an issue with something, such as unintended negative consequences of a drug , what does that mean for the industry and the public? This could be:

• Reduced overhead costs
• Better safety
• Faster production methods
• Improved scaleability

How to describe the commercial and industrial significance of a study, with examples

Commercial significance.

If your technology could be commercially viable, and you’ve got an interest in commercialising it yourself, it is likely that you and your university may not want to immediately publish the study in a journal.

You’ll probably want to consider routes to exploiting the technology and your university may have a “technology transfer” team to help researchers navigate the various options.

However, if instead of publishing a paper you’re submitting a thesis or dissertation then it can be useful to highlight the commercial significance of your work. In this instance you could include statements of commercial significance such as:

The measurement technology described in this study provides state of the art performance and could enable the development of low cost devices for aerospace applications. An example of commercial significance I invented for this post

Industrial significance

First, think about the industrial sectors who could benefit from the developments described in your study.

For example if you’re working to improve battery efficiency it is easy to think of how it could lead to performance gains for certain industries, like personal electronics or electric vehicles. In these instances you can describe the industrial significance relatively easily, based off your findings.

For example:

By utilising abundant materials in the described battery fabrication process we provide a framework for battery manufacturers to reduce dependence on rare earth components. Again, an invented example

For other technologies there may well be industrial applications but they are less immediately obvious and applicable. In these scenarios the best you can do is to simply reframe your research significance statement in terms of potential commercial applications in a broad way.

As a reminder: not all studies should address industrial significance, so don’t try to invent applications just for the sake of it!

Societal Significance

What is the societal significance of a study.

The most broad category of significance is the societal impact which could stem from it.

If you’re working in an applied field it may be quite easy to see a route for your research to impact society. For others, the route to societal significance may be less immediate or clear.

Studies can help with big issues facing society such as:

• Medical applications : vaccines, surgical implants, drugs, improving patient safety. For instance this medical device and drug combination I worked on which has a very direct route to societal significance.
• Political significance : Your research may provide insights which could contribute towards potential changes in policy or better understanding of issues facing society.
• Public health : for instance COVID-19 transmission and related decisions.
• Climate change : mitigation such as more efficient solar panels and lower cost battery solutions, and studying required adaptation efforts and technologies. Also, better understanding around related societal issues, for instance this study on the effects of temperature on hate speech.

How is societal significance measured?

Societal significance at a high level can be quantified by the size of its potential societal effect. Just like a lab risk assessment, you can think of it in terms of probability (or how many people it could help) and impact magnitude.

Societal impact = How many people it could help x the magnitude of the impact

Think about how widely applicable the findings are: for instance does it affect only certain people? Then think about the potential size of the impact: what kind of difference could it make to those people?

Between these two metrics you can get a pretty good overview of the potential societal significance of your research study.

How to describe the societal significance of a study, with examples

Quite often the broad societal significance of your study is what you’re setting the scene for in your Introduction. In addition to describing the existing literature, it is common to for the study’s motivation to touch on its wider impact for society.

For those of us working in healthcare research it is usually pretty easy to see a path towards societal significance.

Our CLOUT model has state-of-the-art performance in mortality prediction, surpassing other competitive NN models and a logistic regression model … Our results show that the risk factors identified by the CLOUT model agree with physicians’ assessment, suggesting that CLOUT could be used in real-world clinicalsettings. Our results strongly support that CLOUT may be a useful tool to generate clinical prediction models, especially among hospitalized and critically ill patient populations. Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

In other domains the societal significance may either take longer or be more indirect, meaning that it can be more difficult to describe the societal impact.

Even so, here are some examples I’ve found from studies in non-healthcare domains:

We examined food waste as an initial investigation and test of this methodology, and there is clear potential for the examination of not only other policy texts related to food waste (e.g., liability protection, tax incentives, etc.; Broad Leib et al., 2020) but related to sustainable fishing (Worm et al., 2006) and energy use (Hawken, 2017). These other areas are of obvious relevance to climate change… AI-Based Text Analysis for Evaluating Food Waste Policies

The continued development of state-of-the art NLP tools tailored to climate policy will allow climate researchers and policy makers to extract meaningful information from this growing body of text, to monitor trends over time and administrative units, and to identify potential policy improvements. BERT Classification of Paris Agreement Climate Action Plans

Top Tips For Identifying & Writing About the Significance of Your Study

• Writing a thesis? Describe the significance of your study in the Introduction and the Conclusion .
• Submitting a paper? Read the journal’s guidelines. If you’re writing a statement of significance for a journal, make sure you read any guidance they give for what they’re expecting.
• Take a step back from your research and consider your study’s main contributions.
• Read previously published studies in your field . Use this for inspiration and ideas on how to describe the significance of your own study
• Discuss the study with your supervisor and potential co-authors or collaborators and brainstorm potential types of significance for it.

Now you’ve finished reading up on the significance of a study you may also like my how-to guide for all aspects of writing your first research paper .

Writing an academic journal paper

I hope that you’ve learned something useful from this article about the significance of a study. If you have any more research-related questions let me know, I’m here to help.

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