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• A Researcher’s Guide To Statistical Significance And Sample Size Calculations

What Does It Mean for Research to Be Statistically Significant?

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What does it mean to be statistically significant, an example of null hypothesis significance testing, measuring statistical significance: understanding the p value (significance level), what factors affect the power of hypothesis test, 1. sample size, 2. significance level, 3. standard deviations, 4. effect size, why is statistical significance important for researchers, does your study need to be statistically significant, practical significance vs. statistical significance, part 1: how is statistical significance defined in research.

The world today is drowning in data.

That may sound like hyperbole but consider this. In 2018, humans around the world produced more than 2.5 quintillion bytes of data—each day. According to some estimates , every minute people conduct almost 4.5 million Google searches, post 511,200 tweets, watch 4.5 million YouTube videos, swipe 1.4 million times on Tinder, and order 8,683 meals from GrubHub. These numbers—and the world’s total data—are expected to continue growing exponentially in the coming years.

For behavioral researchers and businesses, this data represents a valuable opportunity. However, using data to learn about human behavior or make decisions about consumer behavior often requires an understanding of statistics and statistical significance.

Statistical significance is a measurement of how likely it is that the difference between two groups, models, or statistics occurred by chance or occurred because two variables are actually related to each other. This means that a “statistically significant” finding is one in which it is likely the finding is real, reliable, and not due to chance.

To evaluate whether a finding is statistically significant, researchers engage in a process known as null hypothesis significance testing . Null hypothesis significance testing is less of a mathematical formula and more of a logical process for thinking about the strength and legitimacy of a finding.

Imagine a Vice President of Marketing asks her team to test a new layout for the company website. The new layout streamlines the user experience by making it easier for people to place orders and suggesting additional items to go along with each customer’s purchase. After testing the new website, the VP finds that visitors to the site spend an average of $12.63. Under the old layout, visitors spent an average of $12.32, meaning the new layout increases average spending by $0.31 per person. The question the VP must answer is whether the difference of $0.31 per person is significant or something that likely occurred by chance.

To answer this question with statistical analysis, the VP begins by adopting a skeptical stance toward her data known as the null hypothesis . The null hypothesis assumes that whatever researchers are studying does not actually exist in the population of interest. So, in this case the VP assumes that the change in website layout does not influence how much people spend on purchases.

With the null hypothesis in mind, the manager asks how likely it is that she would obtain the results observed in her study—the average difference of $0.31 per visitor—if the change in website layout actually causes no difference in people’s spending (i.e., if the null hypothesis is true). If the probability of obtaining the observed results is low, the manager will reject the null hypothesis and conclude that her finding is statistically significant.

Statistically significant findings indicate not only that the researchers’ results are unlikely the result of chance, but also that there is an effect or relationship between the variables being studied in the larger population. However, because researchers want to ensure they do not falsely conclude there is a meaningful difference between groups when in fact the difference is due to chance, they often set stringent criteria for their statistical tests. This criterion is known as the significance level .

Within the social sciences, researchers often adopt a significance level of 5%. This means researchers are only willing to conclude that the results of their study are statistically significant if the probability of obtaining those results if the null hypothesis were true—known as the p value —is less than 5%.

Five percent represents a stringent criterion, but there is nothing magical about it. In medical research, significance levels are often set at 1%. In cognitive neuroscience, researchers often adopt significance levels well below 1%. And, when astronomers seek to explain aspects of the universe or physicists study new particles like the Higgs Boson they set significance levels several orders of magnitude below .05.

In other research contexts like business or industry, researchers may set more lenient significance levels depending on the aim of their research. However, in all research, the more stringently a researcher sets their significance level, the more confident they can be that their results are not due to chance.

Determining whether a given set of results is statistically significant is only one half of the hypothesis testing equation. The other half is ensuring that the statistical tests a researcher conducts are powerful enough to detect an effect if one really exists. That is, when a researcher concludes their hypothesis was incorrect and there is no effect between the variables being studied, that conclusion is only meaningful if the study was powerful enough to detect an effect if one really existed.

The power of a hypothesis test is influenced by several factors.

Sample size—or, the number of participants the researcher collects data from—affects the power of a hypothesis test. Larger samples with more observations generally lead to higher-powered tests than smaller samples. In addition, large samples are more likely to produce replicable results because extreme scores that occur by chance are more likely to balance out in a large sample rather than in a small one.

Although setting a low significance level helps researchers ensure their results are not due to chance, it also lowers their power to detect an effect because it makes rejecting the null hypothesis harder. In this respect, the significance level a researcher selects is often in competition with power.

Standard deviations represent unexplained variability within data, also known as error. Generally speaking, the more unexplained variability within a dataset, the less power researchers have to detect an effect. Unexplained variability can be the result of measurement error, individual differences among participants, or situational noise.   

A final factor that influences power is the size of the effect a researcher is studying. As you might expect, big changes in behavior are easier to detect than small ones.

Sometimes researchers do not know the strength of an effect before conducting a study. Even though this makes it harder to conduct a well powered study, it is important to keep in mind that phenomena that produce a large effect will lead to studies with more power than phenomena that produce only a small effect.

Statistical significance is important because it allows researchers to hold a degree of confidence that their findings are real, reliable, and not due to chance. But statistical significance is not equally important to all researchers in all situations. The importance of obtaining statistically significant results depends on what a researcher studies and within what context.

Within academic research, statistical significance is often critical because academic researchers study theoretical relationships between different variables and behavior. Furthermore, the goal of academic research is often to publish research reports in scientific journals. The threshold for publishing in academic journals is often a series of statistically significant results.

Outside of academia, statistical significance is often less important. Researchers, managers, and decision makers in business may use statistical significance to understand how strongly the results of a study should inform the decisions they make. But, because statistical significance is simply a way of quantifying how much confidence to hold in a research finding, people in industry are often more interested in a finding’s practical significance than statistical significance.

To demonstrate the difference between practical and statistical significance, imagine you’re a candidate for political office. Maybe you have decided to run for local or state-wide office, or, if you’re feeling bold, imagine you’re running for President.

During your campaign, your team comes to you with data on messages intended to mobilize voters. These messages have been market tested and now you and your team must decide which ones to adopt.

If you go with Message A, 41% of registered voters say they are likely to turn out at the polls and cast a ballot. If you go with Message B, this number drops to 37%. As a candidate, should you care whether this difference is statistically significant at a p value below .05?

The answer is of course not. What you likely care about more than statistical significance is practical significance —the likelihood that the difference between groups is large enough to be meaningful in real life.  

You should ensure there is some rigor behind the difference in messages before you spend money on a marketing campaign, but when elections are sometimes decided by as little as one vote you should adopt the message that brings more people out to vote. Within business and industry, the practical significance of a research finding is often equally if not more important than the statistical significance. In addition, when findings have large practical significance, they are almost always statistically significant too.

Conducting statistically significant research is a challenge, but it’s a challenge worth tackling. Flawed data and faulty analyses only lead to poor decisions. Start taking steps to ensure your surveys and experiments produce valid results by using CloudResearch. If you have the team to conduct your own studies, CloudResearch can help you find large samples of online participants quickly and easily. Regardless of your demographic criteria or sample size, we can help you get the participants you need. If your team doesn’t have the resources to run a study, we can run it for you. Our team of expert social scientists, computer scientists, and software engineers can design any study, collect the data, and analyze the results for you. Let us show you how conducting statistically significant research can improve your decision-making today.

Continue Reading: A Researcher’s Guide to Statistical Significance and Sample Size Calculations

Part 2: How to Calculate Statistical Significance

Part 3: Determining Sample Size: How Many Survey Participants Do You Need?

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Understanding statistical significance in research: a comprehensive guide, the statsig team.

In a world where data-driven decisions reign supreme, understanding statistical significance is a superpower for researchers and businesses alike. By harnessing the power of statistical analysis, you can confidently navigate the vast sea of data and uncover the hidden gems of genuine insights.

Statistical significance is the key to unlocking the true potential of your research. It acts as a trusty compass, guiding you through the noise and helping you distinguish between meaningful patterns and random chance. With statistical significance by your side, you can make informed decisions based on solid evidence rather than relying on guesswork or intuition.

Introduction to statistical significance

Statistical significance is a measure of the reliability and trustworthiness of research findings. It helps determine whether the observed results are likely to be due to a genuine effect or simply the result of random chance. In other words, statistical significance tells you how confident you can be that your findings are real and not just a fluke.

Why is statistical significance so important in research? Imagine you're conducting a study to determine whether a new medication is effective in treating a specific condition. You collect data from a sample of patients and analyze the results. Statistical significance helps you determine whether the observed improvements in the treatment group are genuinely due to the medication or if they could have occurred by chance.

By calculating statistical significance, you can make informed decisions based on your research data. If the results are statistically significant, you can be confident that the observed effects are real and not just random noise. This knowledge empowers you to take action, whether it's implementing a new strategy, launching a product, or pursuing further research.

Moreover, statistical significance helps researchers and decision-makers avoid making false conclusions based on incomplete or misleading data. It provides a standardized way to evaluate the strength and reliability of research findings, ensuring that decisions are based on solid evidence rather than hunches or guesswork.

In essence, statistical significance is a powerful tool that helps you navigate the complexities of research and data analysis. By understanding and applying the concepts of statistical significance, you can make more informed decisions, uncover meaningful insights, and drive positive change in your field.

Fundamentals of hypothesis testing

Hypothesis testing is a statistical method that helps determine if a claim about a population is true. It involves formulating two hypotheses: the null hypothesis (H0) and the alternative hypothesis (Ha).

The null hypothesis assumes no significant difference or effect exists between variables. In contrast, the alternative hypothesis states that a significant difference or effect is present.

To conduct a hypothesis test, you first state the null and alternative hypotheses. Then, you choose a significance level (α) , typically 0.05 or 0.01, which represents the probability of rejecting the null hypothesis when it's actually true (Type I error).

Selecting an appropriate significance level is crucial before collecting data and performing the test. A smaller α reduces the chances of a Type I error but increases the likelihood of a Type II error (failing to reject a false null hypothesis).

After defining the hypotheses and significance level, you collect data and calculate a test statistic . This statistic is compared to a critical value determined by the chosen α.

If the test statistic falls within the critical region, you reject the null hypothesis in favor of the alternative. Otherwise, you fail to reject the null hypothesis.

It's important to note that failing to reject the null hypothesis doesn't necessarily mean it's true. It simply means there's insufficient evidence to support the alternative hypothesis at the chosen significance level.

Understanding statistical significance in research is essential for making informed decisions based on data. By properly formulating hypotheses, selecting an appropriate α, and interpreting results, you can determine if observed differences are likely due to chance or represent genuine effects.

Feature flags and experimentation platforms can help streamline the process of hypothesis testing in product development. These tools allow teams to easily set up and run controlled experiments, collect data, and analyze results to make data-driven decisions.

For startups looking to implement robust experimentation practices, Statsig's startup program offers resources and support to get started with advanced analytics and experimentation capabilities.

Calculating statistical significance

P-values are the cornerstone of statistical significance . They represent the probability of observing results as extreme as those measured, assuming the null hypothesis is true. The lower the p-value, the more significant the result.

Various statistical tests are used to calculate p-values, depending on the type of data and the research question:

T-tests compare means between two groups (e.g., treatment vs. control).

Chi-square tests assess relationships between categorical variables (e.g., gender and survey responses).

Z-tests compare a sample mean to a known population mean.

ANOVA (Analysis of Variance) compares means across multiple groups simultaneously.

To calculate statistical significance, follow these steps:

Collect and analyze data : Gather relevant data and perform appropriate statistical tests.

Determine the significance level : Choose a threshold (e.g., 0.05) for rejecting the null hypothesis.

Compare p-values to the significance level : If the p-value is less than or equal to the significance level, the result is statistically significant.

Interpret the results : Assess the practical implications of statistically significant findings.

When conducting research, it's crucial to understand what is statistical significance . It helps distinguish genuine effects from random chance, ensuring that conclusions are reliable and actionable. By calculating p-values using appropriate statistical tests, researchers can determine the significance of their results and make informed decisions based on the data.

However, statistical significance alone doesn't guarantee practical relevance. Researchers must also consider the magnitude of the effect and its real-world implications. A statistically significant result with a small effect size may not be as meaningful as a non-significant result with a large effect size.

To ensure the accuracy and reliability of statistical significance in research, it's essential to:

Use appropriate sample sizes and sampling techniques

Control for confounding variables

Correctly interpret p-values and effect sizes

Replicate findings across multiple studies

By following these guidelines, researchers can harness the power of statistical significance to uncover meaningful insights and drive impactful decisions. Whether you're conducting academic research or analyzing business data, understanding what is statistical significance in research is key to making data-driven decisions with confidence.

Interpreting results and avoiding common mistakes

Properly interpreting p-values and statistical significance is crucial for making informed decisions. A p-value represents the probability of observing results as extreme as those measured, assuming the null hypothesis is true. The lower the p-value, the more likely the results are statistically significant .

However, it's essential to consider practical relevance alongside statistical significance. A statistically significant result may not always translate to a meaningful impact in real-world applications. Misinterpreting p-values as a measure of the magnitude or importance of a change is a common pitfall.

Sample size and external factors also play a vital role in interpreting results. Small sample sizes can lead to false-negative results, while excessively large samples may make minor differences appear statistically significant . External variables such as seasonality, marketing campaigns, or technical issues should be accounted for to ensure accurate interpretation.

When determining what is statistical significance in research, it's crucial to strike a balance between statistical validity and practical implications. Researchers should carefully consider the context, sample size, and potential confounding factors to draw meaningful conclusions . Collaborating with experienced statisticians can help navigate the complexities of statistical significance and avoid common mistakes.

By understanding what is statistical significance in research and applying it judiciously, you can make data-driven decisions with confidence. Embrace the power of statistical significance while remaining mindful of its limitations . With a solid grasp of statistical concepts and a pragmatic approach, you'll be well-equipped to uncover genuine insights and drive impactful changes in your research endeavors.

Applications of statistical significance in research

Statistical significance is a crucial tool across various fields, including medicine, psychology, and business. In medical research , statistical significance helps determine the effectiveness of treatments or interventions. Researchers use it to assess whether observed differences between treatment and control groups are likely due to chance or the intervention itself.

In psychology , statistical significance is employed to study human behavior and mental processes. Psychologists rely on it to draw meaningful conclusions from experiments and surveys, ensuring that observed effects are not merely coincidental.

Business decision-makers also leverage statistical significance to make data-driven choices. They use it to evaluate the impact of marketing campaigns, product features, or pricing strategies on key metrics like sales or customer retention.

Statistical significance is particularly valuable in A/B testing and product development. A/B tests compare two versions of a product or feature to determine which performs better. By calculating statistical significance, teams can confidently decide whether to implement a change or stick with the original version.

For example, an e-commerce company might run an A/B test comparing two different checkout page designs. If the new design leads to a statistically significant increase in conversions, the company can roll it out to all users with confidence.

Real-world examples demonstrate the power of statistical significance in driving important discoveries and decisions. In a famous medical study, the Women's Health Initiative used statistical significance to show that hormone replacement therapy increased the risk of breast cancer and heart disease in postmenopausal women. This finding led to a dramatic shift in how these conditions were treated.

Similarly, Netflix used statistical significance in A/B tests to optimize its recommendation algorithm. By comparing different versions of the algorithm and measuring their impact on user engagement, Netflix was able to create a highly personalized experience that keeps subscribers coming back.

Understanding what statistical significance is in research is crucial for making sound decisions based on data. Whether you're a researcher, product manager, or business leader , grasping the concepts behind statistical significance can help you navigate the complexities of data analysis and experimentation.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Refresher on Statistical Significance

It’s too often misused and misunderstood.

When you run an experiment or analyze data, you want to know if your findings are “significant.” But business relevance (i.e., practical significance) isn’t always the same thing as confidence that a result isn’t due purely to chance (i.e., statistical significance). This is an important distinction; unfortunately, statistical significance is often misunderstood and misused in organizations today. And yet because more and more companies are relying on data to make critical business decisions, it’s an essential concept for managers to understand.

• Amy Gallo is a contributing editor at Harvard Business Review, cohost of the Women at Work podcast , and the author of two books: Getting Along: How to Work with Anyone (Even Difficult People) and the HBR Guide to Dealing with Conflict . She writes and speaks about workplace dynamics. Watch her TEDx talk on conflict and follow her on LinkedIn . amyegallo

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Statistics By Jim

Making statistics intuitive

Statistical Significance: Definition & Meaning

By Jim Frost 5 Comments

What is Statistical Significance?

Statistical significance is the goal for most researchers analyzing data. But what does statistically significant mean? Why and when is it important to consider? How do P values fit in with statistical significance? I’ll answer all these questions in this blog post!

Evaluate statistical significance when using a sample to estimate an effect in a population . It helps you determine whether your findings are the result of chance versus an actual effect of a variable of interest.

Statistical significance indicates that an effect you observe in a sample is unlikely to be the product of chance. For statistically significant results, you can conclude that an effect you observe in a sample also exists in the population.

Let’s dig into statistical significance more deeply!

Why We Need to Assess Statistical Significance

In most research studies, the investigators evaluate an effect of some sort. It can be the effectiveness of a new medication, the strength of a product, the relationship between variables, etc. There is some benefit or relationship that they hope to find. Learn about Effect Sizes in Statistics .

When using a sample to estimate an effect, researchers need to evaluate statistical significance.

Researchers typically use representative samples in experiments because measuring an entire population is almost always impractical. Even though they’re using a sample, they really want to determine if that effect exists in the whole population. Discovering an effect that exists in only the relatively small group of study participants doesn’t help move science forward!

While samples are manageable, they introduce sampling error because you’re not appraising the whole population.

When you draw a random sample from a population, sampling error is the difference between a sample estimate and the population value (known as a parameter ). This difference occurs by chance, literally the luck of the draw, and it is unavoidable when working with samples. It is this sampling error that creates the need to assess statistical significance.

Unfortunately, sampling error virtually guarantees that a sample estimate of the effect won’t equal the correct population parameter exactly. In fact, if you were to draw many random samples from a population and perform the same experiment, you’d get different results each time due to sampling error.

Because the results change depending on the sample you draw, you need a way to account for sampling error. After all, you might see a nice effect in one sample but not another. Statistical significance helps you evaluate the potential role of sampling error in your results.

Related posts : Random Sampling , Representative Samples , and Sampling Error

What does Statistically Significant Mean?

Sampling error forces us to consider statistical significance. When you draw a random sample from a population, there is always a chance that sampling error created the observed effect.

Imagine a hypothetical study for a medicine that we know is entirely useless. In other words, the effect size is zero. No difference exists at the population level between subjects who take the vaccine and those who don’t. However, thanks to sampling error, we’re bound to see some difference between those two groups even though there is no vaccine effect.

How do we know whether the sample estimate reflects sampling error or a true effect?

Statistical significance tells us that the sample effect is unlikely to be a mirage caused by sampling error. When we have statistically significant results, we conclude it is an actual effect existing in the population.

Definition of Statistically Significant

The definition of statistically significant is that the sample effect is unlikely to be caused by chance (i.e., sampling error). In other words, what we see in the sample likely reflects an effect or relationship that exists in the population.

Using a more statistically correct technical definition, statistical significance relates to the following conditional probability.

If there is no effect at the population level, sampling error is unlikely to have produced your sample results.

The flip side of statistical significance is non-significant results. This condition indicates you can’t conclude the sample effect exists in the population. It would not be surprising if sampling error created the appearance of an effect in your sample by chance alone. If that is the case, the benefit you see in your sample does not exist in the population.

Statistical Significance Example

Suppose you perform an experiment with a new drug that purportedly increases intelligence. The treatment group takes the drug, while the control group does not. After the experiment, you find that the treatment group has an average IQ that is 10 points higher than the control group. That’s your sample estimate for the treatment effect.

That looks great!

However, random chance might have selected a sample for the treatment group that, by sheer luck, got better results. In other words, the difference might not be due to the medicine but sampling error instead.

If you perform the experiment again, would you get similarly great results?

By performing the correct hypothesis test, you can determine the likelihood of obtaining your sample results if the medication has no effect.

For this study, statistically significant results indicate that the sample effect of 10 IQ points was unlikely to occur if there was no effect in the population. Therefore, you can be confident that the drug has a real effect.

Conversely, non-statistically significant results indicate the sample effect might be sampling error rather than an actual effect. Random chance could have conspired to create the illusion of an effect in your sample. If the medicine’s effect does not exist in the population, it won’t have the benefits we expect based on our sample results.

That’s why determining statistical significance is crucial.

P values and Statistical Significance

Statistical significance occurs in the context of hypothesis testing . These analyses are a form of inferential statistics —using a sample to estimate the properties of a population. As I discussed previously, we want to determine whether the sample effect also exists in the population. Or is the effect a mirage produced by chance during the random sampling process?

Hypothesis testing provides the tools to evaluate statistical significance. These two tools are p-values and the significance level .

P-values : The probability of obtaining the observed sample effect or larger if there is no effect in the population.

Significance level : An evidentiary standard that researchers select as the threshold for statistical significance.

P-values indicate the strength of the evidence against sampling error producing the sample effect. And the significance level defines how strong the evidence must be to reject the notion that the sample effect is a mirage caused by sampling error. Therefore, you need to compare your p-value to the significance level to determine statistical significance.

Your results are statistically significant if the p-value is ≤ your significance level. For example, if your p-value is 0.01 and your significance level is 0.05, your results are statistically significant.

Learn more about the rationale behind How Hypothesis Tests Work .

Related posts : Interpreting P-values and the Significance Level .

Caution: Statistical Significance ≠ Practical Significance

Finally, it might seem logical to think that statistically significant results indicate you have a large effect that is meaningful in a practical, real-world sense. However, that is not necessarily true.

Effect size is only one factor in assessing statistical significance. Sample size and data variability are two others. You can obtain statistically significant results when you have a negligible effect but have a large sample size and low variability. In this situation, the effect likely exists but doesn’t amount to much in the real world.

Use subject-area knowledge to assess the practical significance of your findings.

To learn more about this distinction and how to assess it, read my post about Practical vs. Statistical Significance .

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Reader Interactions

February 10, 2023 at 11:51 am

Quick question, had an a/b test that I was running and checked the confidence level for the data to be ~79%. This was after running the test for two weeks. I went back and looked a week later and the confidence level was 11%. Could this mean I hit confidence sometime during the week before I returned and looked at my data? If so would this 11% number represent a confidence on a bigger population. Trying to figure out how a confidence level can drop so much after running a test for so long. Not sure if there is any theory or concept that could explain this.

Thanks, Gary

February 11, 2023 at 11:35 pm

When you run a hypothesis test and use it to create a confidence interval, you set the confidence level at a specific value, such as 95%. Your software should not be changing the confidence level over time. So, I don’t know what is happening. I have no theories other than your software is doing something it shouldn’t do.

I’m not sure if you’re doing the following or not. But observing incoming data until you reach statistical significance is a form of cherry picking. This practice tends to produce significant results even when it’s not warranted. I know it’s tempting because it’s exciting when you see significant results. However, it won’t be as exciting when you use the results of this cherry-picking method to implement changes and the improvements fail to materialize.

Instead, picked a fixed amount of time to run the test, and then assess results at that point.

November 7, 2022 at 3:07 pm

November 7, 2022 at 1:14 am

Thank you Prof for this post. One area that my students struggle with is understanding the relationship between confidence level (CI) and the p-value vs significance level. Any intuitive explanation on this?

November 7, 2022 at 3:48 pm

Yes, I’ve written a post that covers that exactly! 🙂

Hypothesis Testing and Confidence Intervals

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How To Write a Significance Statement for Your Research

A significance statement is an essential part of a research paper. It explains the importance and relevance of the study to the academic community and the world at large. To write a compelling significance statement, identify the research problem, explain why it is significant, provide evidence of its importance, and highlight its potential impact on future research, policy, or practice. A well-crafted significance statement should effectively communicate the value of the research to readers and help them understand why it matters.

Updated on May 4, 2023

A significance statement is a clearly stated, non-technical paragraph that explains why your research matters. It’s central in making the public aware of and gaining support for your research.

Write it in jargon-free language that a reader from any field can understand. Well-crafted, easily readable significance statements can improve your chances for citation and impact and make it easier for readers outside your field to find and understand your work.

Read on for more details on what a significance statement is, how it can enhance the impact of your research, and, of course, how to write one.

What is a significance statement in research?

A significance statement answers the question: How will your research advance scientific knowledge and impact society at large (as well as specific populations)? 

You might also see it called a “Significance of the study” statement. Some professional organizations in the STEM sciences and social sciences now recommended that journals in their disciplines make such statements a standard feature of each published article. Funding agencies also consider “significance” a key criterion for their awards.

Read some examples of significance statements from the Proceedings of the National Academy of Sciences (PNAS) here .

Depending upon the specific journal or funding agency’s requirements, your statement may be around 100 words and answer these questions:

1. What’s the purpose of this research?

2. What are its key findings?

3. Why do they matter?

4. Who benefits from the research results?

Readers will want to know: “What is interesting or important about this research?” Keep asking yourself that question.

Where to place the significance statement in your manuscript

Most journals ask you to place the significance statement before or after the abstract, so check with each journal’s guide. 

This article is focused on the formal significance statement, even though you’ll naturally highlight your project’s significance elsewhere in your manuscript. (In the introduction, you’ll set out your research aims, and in the conclusion, you’ll explain the potential applications of your research and recommend areas for future research. You’re building an overall case for the value of your work.)

Developing the significance statement

The main steps in planning and developing your statement are to assess the gaps to which your study contributes, and then define your work’s implications and impact.

Identify what gaps your study fills and what it contributes

Your literature review was a big part of how you planned your study. To develop your research aims and objectives, you identified gaps or unanswered questions in the preceding research and designed your study to address them.

Go back to that lit review and look at those gaps again. Review your research proposal to refresh your memory. Ask:

• How have my research findings advanced knowledge or provided notable new insights?
• How has my research helped to prove (or disprove) a hypothesis or answer a research question?
• Why are those results important?

Consider your study’s potential impact at two levels: 

• What contribution does my research make to my field?
• How does it specifically contribute to knowledge; that is, who will benefit the most from it?

Define the implications and potential impact

As you make notes, keep the reasons in mind for why you are writing this statement. Whom will it impact, and why?

The first audience for your significance statement will be journal reviewers when you submit your article for publishing. Many journals require one for manuscript submissions. Study the author’s guide of your desired journal to see its criteria ( here’s an example ). Peer reviewers who can clearly understand the value of your research will be more likely to recommend publication. 

Second, when you apply for funding, your significance statement will help justify why your research deserves a grant from a funding agency . The U.S. National Institutes of Health (NIH), for example, wants to see that a project will “exert a sustained, powerful influence on the research field(s) involved.” Clear, simple language is always valuable because not all reviewers will be specialists in your field.

Third, this concise statement about your study’s importance can affect how potential readers engage with your work. Science journalists and interested readers can promote and spread your work, enhancing your reputation and influence. Help them understand your work.

You’re now ready to express the importance of your research clearly and concisely. Time to start writing.

How to write a significance statement: Key elements 

When drafting your statement, focus on both the content and writing style.

• In terms of content, emphasize the importance, timeliness, and relevance of your research results. 
• Write the statement in plain, clear language rather than scientific or technical jargon. Your audience will include not just your fellow scientists but also non-specialists like journalists, funding reviewers, and members of the public. 

Follow the process we outline below to build a solid, well-crafted, and informative statement. 

Get started

Some suggested opening lines to help you get started might be:

• The implications of this study are… 
• Building upon previous contributions, our study moves the field forward because…
• Our study furthers previous understanding about…

Alternatively, you may start with a statement about the phenomenon you’re studying, leading to the problem statement.

Include these components

Next, draft some sentences that include the following elements. A good example, which we’ll use here, is a significance statement by Rogers et al. (2022) published in the Journal of Climate .

1. Briefly situate your research study in its larger context . Start by introducing the topic, leading to a problem statement. Here’s an example:

‘Heatwaves pose a major threat to human health, ecosystems, and human systems.”

2. State the research problem.

“Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages.”

3. Tell what your study does to address it.

“We assess recent changes in the occurrence of simultaneous large heatwaves.”

4. Provide brief but powerful evidence to support the claims your statement is making , Use quantifiable terms rather than vague ones (e.g., instead of “This phenomenon is happening now more than ever,” see below how Rogers et al. (2022) explained it). This evidence intensifies and illustrates the problem more vividly:

“Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia.

5. Relate your study’s impact to the broader context , starting with its general significance to society—then, when possible, move to the particular as you name specific applications of your research findings. (Our example lacks this second level of application.) 

“Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.”

Refine your English

Don’t understate or overstate your findings – just make clear what your study contributes. When you have all the elements in place, review your draft to simplify and polish your language. Even better, get an expert AJE edit . Be sure to use “plain” language rather than academic jargon.

• Avoid acronyms, scientific jargon, and technical terms 
• Use active verbs in your sentence structure rather than passive voice (e.g., instead of “It was found that...”, use “We found...”)
• Make sentence structures short, easy to understand – readable
• Try to address only one idea in each sentence and keep sentences within 25 words (15 words is even better)
• Eliminate nonessential words and phrases (“fluff” and wordiness)

Enhance your significance statement’s impact

Always take time to review your draft multiple times. Make sure that you:

• Keep your language focused
• Provide evidence to support your claims
• Relate the significance to the broader research context in your field

After revising your significance statement, request feedback from a reading mentor about how to make it even clearer. If you’re not a native English speaker, seek help from a native-English-speaking colleague or use an editing service like AJE to make sure your work is at a native level.

Understanding the significance of your study

Your readers may have much less interest than you do in the specific details of your research methods and measures. Many readers will scan your article to learn how your findings might apply to them and their own research. 

Different types of significance

Your findings may have different types of significance, relevant to different populations or fields of study for different reasons. You can emphasize your work’s statistical, clinical, or practical significance. Editors or reviewers in the social sciences might also evaluate your work’s social or political significance.

Statistical significance means that the results are unlikely to have occurred randomly. Instead, it implies a true cause-and-effect relationship.

Clinical significance means that your findings are applicable for treating patients and improving quality of life.

Practical significance is when your research outcomes are meaningful to society at large, in the “real world.” Practical significance is usually measured by the study’s  effect size . Similarly, evaluators may attribute social or political significance to research that addresses “real and immediate” social problems.

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The Savvy Scientist

Experiences of a London PhD student and beyond

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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• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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What it means when scientists say their results are ‘significant’

Post-Doctoral Researcher, South Australian Health & Medical Research Institute

Disclosure statement

Yazad Irani does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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This article is part of the series This is research , where we ask academics to share and discuss open access articles that reveal important aspects of science. Today’s piece looks at statistics, and how to interpret them for meaning in the real world.

Let’s face it, scientific papers aren’t exactly page turners. They are written by scientists, for scientists, and often in a language that seems to only vaguely resemble English.

And perhaps one of the most daunting aspects of a scientific paper is the statistics (“stats”) section.

But what do stats really mean in the real world? Here’s an example from leukaemia research to help you break it down.

Read more: Our survey found 'questionable research practices' by ecologists and biologists – here's what that means

Strength of results

Stats are key to good research – they help researchers determine whether the results observed are strong enough to be due to an important scientific phenomenon.

As a research student I would always look for the magic number which indicates statistically significant differences in my experiments: most people agree this number to be 0.05 (you may see this in a paper written as p < 0.05).

When comparing two groups in a scientific study, statistical significance indicated by a p-value of less than 0.05 means that, in the case where there was no real difference between groups, there’s less than a 5% chance of the observed result arising.

But the focus on looking for statistically significant differences can blind us to the bigger picture. As I advanced through my scientific training I learnt to look for biologically significant differences.

Read more: The curious case of the missing workplace teaspoons

Biological significance

Biological significance addresses the question of whether the statistical difference actually means anything in terms of a real outcome, like a disease. Can the result explain how the disease is caused? Does it provide a new avenue to treat the disease? Basically, is it relevant?

A recent paper published in the journal Leukaemia will help explain my point. The paper looked at why some people are able to stop their treatment for chronic myeloid leukaemia without the cancer coming back, while in others the cancer relapsed.

The key finding of this study was that patients who did not relapse had a higher proportion of natural killer cells compared to patients that did relapse.

Natural killer cells are a type of immune cell that controls viral infections and tumours. So, the more cells there are to kill the cancer, the less likely the cancer was to relapse – makes sense!

This finding has the potential to guide doctors in seeing which patients are likely to remain cancer-free after stopping treatment. This is definitely biologically significant.

Read more: My cancer is in remission – does this mean I'm cured?

Not so relevant

Another result from the same paper (Figure 3a if you want to click through to the data) shows a statistically significant difference in a sub-type of natural killer cells (called adaptive natural killer cells). But is this difference biologically relevant?

At this stage there is little evidence of a role for adaptive natural killer cells in the context of leukaemia. Also, the difference between the groups is relatively small, with a large variation within the groups (there are large error bars on the graph).

These factors make it more likely that the differences may be due to the mathematics involved in the statistical test rather than a biological effect. As with any new finding, time and further studies will be vital in working out whether this result actually means anything.

Act like an expert

So how do you pick if the statistical differences have biological value? Being a highly trained expert in the field certainly helps.

Another way to determine if the findings in a paper have biological relevance is to look for other papers that show similar results. If a result is “real” it should be found by other scientists who will build on it and publish more papers.

This means there will be lots of papers for you to read and apply your new-found passion for statistics.

The open access research paper for this analysis is Increased proportion of mature NK cells is associated with successful imatinib discontinuation in chronic myeloid leukemia .

The definition of statistical significance has been edited since this article was first published.

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What Is Statistical Significance?

Understanding statistical significance, examples of statistical significance, how is statistical significance determined, what is p-value, how is statistical significance used.

• Corporate Finance
• Financial Analysis

Statistical Significance: What It Is, How It Works, With Examples

Statistical significance is a determination made by an analyst that the results in the data are not explainable by chance alone. Statistical  hypothesis testing  is the method by which the analyst makes this determination. This test provides a p-value , which is the probability of observing results as extreme as those in the data, assuming the results are truly due to chance alone. A p-value of 5% or lower is often considered to be statistically significant.

Key Takeaways

• Statistical significance is a determination that a relationship between two or more variables is caused by something other than chance.
• Statistical significance is used to provide evidence concerning the plausibility of the null hypothesis, which hypothesizes that there is nothing more than random chance at work in the data.
• Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.
• Generally, a p-value of 5% or lower is considered statistically significant.

Investopedia / Paige McLaughlin

Statistical significance is a determination of the  null hypothesis , which suggests that the results are due to chance alone. A data set provides statistical significance when the p-value is sufficiently small.

When the p-value is large, then the results in the data are explainable by chance alone , and the data are deemed consistent with (while not proving) the null hypothesis.

When the p-value is sufficiently small (typically 5% or less), the results are not easily explained by chance alone, and the data are deemed inconsistent with the null hypothesis. In this case, the null hypothesis of chance alone as an explanation of the data is rejected in favor of a more systematic explanation.

Statistical significance is often used for new pharmaceutical drug trials, to test vaccines, and in the study of pathology for effectiveness testing and to inform investors on how successful the company is at releasing new products.

Suppose Alex, a financial analyst, is curious as to whether some investors had advance knowledge of a company's sudden failure. Alex decides to compare the average of daily market returns prior to the company's failure with those after to see if there is a statistically significant difference between the two averages.

The study's p-value was 28% (>5%), indicating that a difference as large as the observed (-0.0033 to +0.0007) is not unusual under the chance-only explanation. Thus, the data did not provide compelling evidence of advance knowledge of the failure. On the other hand, if the p-value were 0.01% (much less than 5%), then the observed difference would be very unusual under the chance-only explanation. In this case, Alex may decide to reject the null hypothesis and to investigate further whether some traders had advance knowledge.

Statistical significance is also used to test new medical products, including drugs, devices, and vaccines. Publicly available reports of statistical significance also inform investors on how successful the company is at releasing new products.

Say, for example, a pharmaceutical leader in diabetes medication reported that there was a statistically significant reduction in type 1 diabetes when it tested its new insulin. The test consisted of 26 weeks of randomized therapy among diabetes patients, and the data gave a p-value of 4%. This signifies to investors and regulatory agencies that the data show a statistically significant reduction in type 1 diabetes.

Stock prices of pharmaceutical companies are often affected by announcements of the statistical significance of their new products.

Statistical hypothesis testing is used to determine whether the data is statistically significant. In other words, whether or not the phenomenon can be explained as a byproduct of chance alone. Statistical significance is a determination about the null hypothesis, which posits that the results are due to chance alone. The rejection of the null hypothesis is needed for the data to be deemed statistically significant.

A p-value is a measure of the probability that an observed difference could have occurred just by random chance. When the p-value is sufficiently small (e.g., 5% or less), then the results are not easily explained by chance alone and the null hypothesis can be rejected. When the p-value is large, then the results in the data are explainable by chance alone, and the data is deemed consistent with (while proving) the null hypothesis. 

Statistical significance is often used to test the effectiveness of new medical products, including drugs, devices, and vaccines. Publicly available reports of statistical significance also inform investors on how successful the company is at releasing new products. Stock prices of pharmaceutical companies are often affected strongly by announcements of the statistical significance of their new products.

Steven Tenny and Ibrahim Abdelgawad. " Statistical Significance. " StatPearls Publishing, 2022.

American Diabetes Association. " Efficacy and Safety of Fast-Acting Aspart Compared With Insulin Aspart, Both in Combination With Insulin Degludec, in Children and Adolescents With Type 1 Diabetes: The Onset 7 Trial ."

Thomas J. Hwang. " Stock Market Returns and Clinical Trial Results of Investigational Compounds: An Event Study Analysis of Large Biopharmaceutical Companies. " PLOS ONE, 2013.

Rothenstein, Jeffrey et al. " Company Stock Prices Before and After Public Announcements Related to Oncology Drugs. " Journal of the National Cancer Institute, vol. 103, no. 20, October 2011, pp. 1507-1512.

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Statistical Significance: Definition, Calculation, Importance

Kate williams.

Last Updated:  

14 November 2023

Table Of Contents

What is Statistical Significance?

• Basic Terms
• Null Hypothesis
• Statistical Significance
• How to Determine
• Limitations
• Role in Hypothesis Testing

What separates a genuine discovery from mere chance? Statisticians agree that in data analysis, statistical significance holds a prime position. It is essential to know all about it if you wish to draw accurate conclusions. So, what does it mean, and how is it calculated?

As we know, it can get messy if you do not understand the terms associated with it. So, why not start with the basics? I have included the statistical jargon used in this blog.

Basic Terms and Definitions

Statistical significance:.

• It helps us know if our findings are authentic or just random chance.
• We calculate it using a p-value, usually less than or equal to 0.05 for significance.
• The p-value shows us if our results have a meaning.
• Also, a small p-value (below 0.05) means strong evidence supports our findings.

Null-Hypothesis:

• This hypothesis suggests there’s no difference between groups or conditions.
• Above all, researchers test it to find natural patterns and differences in the data.

Statistical Significance Calculator:

• It’s a user-friendly tool that simplifies complex calculations. The calculator gives researchers the p-value without needing advanced math.
• Researchers use it to quickly analyze their data, making the process efficient and accurate.

Effect Size:

• Effect size measures the real impact of our results.
• It helps us understand the practical significance of our findings beyond just statistical significance.

Confidence Interval:

• A confidence interval gives us a range indicating the uncertainty of our results.
• It shows the probable range within which our result lies, offering a clearer picture of the data.

Type I Error and Type II Error:

• Type I is a false positive, while Type II is a false negative.
• Striking a balance between these errors is essential for accurate conclusions in research.

Alpha Level (α)

• The alpha level (usually set at 0.05) is the threshold for significance in hypothesis testing.
• Choosing an appropriate alpha level is vital. It influences our confidence in research outcomes.

Critical Region:

• The critical region shows the area where results are considered significant.
• If our results fall inside this region, they are meaningful, guiding us toward valuable conclusions.

What is a Hypothesis?

A hypothesis is like an educated guess in science!

As mentioned above, it’s a clear and specific statement that predicts what might happen in an experiment or study. Scientists use hypotheses to guide their research and predict the outcomes they expect to see.

Components of a Hypothesis

Statement: A hypothesis is a concise statement that explains a relationship between variables. It’s usually based on prior knowledge, observations, or existing theories.

Testable: A reasonable hypothesis must be testable. We must be able to get to the “right” or “wrong” through experiments or observations. Scientists need to design experiments that can either support or refute the hypothesis.

Precise Prediction: A hypothesis includes a clear prediction about the experiment’s outcome. It specifies what the researcher expects to observe if the hypothesis is correct.

Let’s say a scientist is curious about whether plants grow better with sunlight. Their hypothesis could be: “Plants exposed to sunlight will grow taller than plants kept in the dark.” This statement is specific, testable, and predicts that sunlight positively affects plant growth.

Importance of Hypotheses

• Guiding Research: Hypotheses provide a roadmap for scientific investigations. They help researchers focus their experiments, ensuring they collect relevant data to support or refute the hypothesis.
• Predicting Outcomes: By making predictions, hypotheses allow scientists to anticipate what they might find. This foresight helps in interpreting results and drawing meaningful conclusions.
• Advancing Knowledge: Testing hypotheses contributes to the accumulation of scientific knowledge. Whether a hypothesis is confirmed or rejected, the results provide valuable insights, leading to a deeper understanding of the natural world.

What is a Null Hypothesis?

A null hypothesis is the opposite of the main guess in a scientific experiment. It’s a statement suggesting that there is no significant difference or effect between groups or conditions.

In simpler terms, it represents the idea that any observed differences are just due to chance and not because of a real relationship or effect.

Components of a Null Hypothesis

Neutral Statement: The null hypothesis is a neutral and straightforward statement. It doesn’t predict a specific outcome but asserts that there is no difference.

Comparison Basis: It serves as a benchmark for comparison. Scientists test their experimental results against the null hypothesis to see if there is enough evidence to reject it in favor of their hypothesis.

In the case of the plant growth experiment, the null hypothesis could be: “There is no significant difference in the height of plants grown in sunlight compared to plants grown in the dark.” This statement implies that any difference in plant height observed between the two groups is merely coincidental and not because of sunlight.

Importance of Null-Hypotheses:

• Critical Comparison: By comparing experimental results to the null hypothesis, scientists determine if their findings are statistically significant. If the results significantly differ from what the null hypothesis predicts, it suggests a meaningful relationship.
• Avoiding Bias: Having a null hypothesis prevents bias in interpreting results. Researchers remain open to the possibility of no effect, ensuring objective analysis.
• Scientific Rigor: Including a null hypothesis in experiments adds rigor to scientific investigations. It sets a standard that results must surpass to be considered genuinely significant.

Okay, now let’s get to business!

Statistical significance helps scientists trust their findings!

By definition, being statistically significant means that the results of a study are probably actual, not random.

When the p-value is low (usually below 0.05), it shows the results are meaningful. It’s like a stamp of approval for research conclusions.

So, what does being statistically significant mean?

Picture this: You’ve conducted an experiment, and the results are astounding. But are they genuine or merely coincidental? To be statistically significant means your findings aren’t a random chance at play. It signifies a meaningful pattern in the data, lending credibility to your research.

Scientists use statistical significance to determine if results are meaningful and not just due to chance. This helps researchers understand if differences or patterns in their data are accurate. It answers the question: “Is what we found in our research genuine, or could it have happened randomly?

Still not clear? Let’s break it down a little further. 

Imagine a study comparing two groups: Group A received a new drug, and Group B received a placebo. After analyzing the data, researchers found that patients in Group A had significantly lower cholesterol levels than Group B, with a p-value of 0.02 (less than 0.05).

This indicates that the difference in cholesterol levels between the groups is statistically significant. This suggests that the new drug effectively reduces cholesterol.

Statistical Significance Calculator

Crunching numbers can be a little daunting, right? Well, it doesn’t have to be (given that you have the right guide.)

A statistical significance calculator is a handy tool for researchers. It does the complex math for them! Scientists feed in their data, and the calculator quickly tells them whether the results are significant. It’s user-friendly and saves time, ensuring accurate analysis without the headache of intricate calculations.

Furthermore, a high variance in the population shows a high chance of error and sampling bias .

The formula for statistical significance varies based on the statistical test being used. However, a general procedure for many tests involves calculating a test statistic (like t or z) and comparing it to a critical value from a statistical table or using the software.

Test Statistic = Observed Value−Expected Value/ Standard Error

In this formula, the test statistic represents how many standard deviations the observed value is from the expected value. The smaller the test statistic, the more likely the results are statistically significant. Researchers compare this statistic with critical importance to determine significance.

Importance of Statistical Significance

• Validates Research Findings: Statistical significance confirms that the observed results are not due to chance, adding credibility to the study’s outcomes.
• Informs Informed Decision-Making: It provides a basis for making informed decisions in various fields, guiding policies, strategies, and interventions.
• Supports Research Conclusions: When results are statistically significant, they keep the conclusions drawn from the study, making them more robust and reliable.
• Facilitates Accurate Comparisons: Statistical significance allows researchers to accurately compare different groups, treatments, or conditions, enabling meaningful comparisons.
• Optimizes Resource Utilization: By focusing efforts on statistically significant findings, resources such as time, money, and manpower are used efficiently, avoiding waste on less meaningful outcomes.
• Builds Trust in Research: Studies with statistically significant results are more trustworthy, gaining the confidence of peers, stakeholders, and the general public.

Regarding data analysis, advanced tools such as SurveySparrow simplify complex statistical processes. For instance, for data collection, it is the perfect tool that lets you create, share, and collate data. Further, you can analyze and act upon the insights gained. Yes, all on the same platform.

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How to Determine Statistical Significance

Take a look at this 8-step guide:

1. Formulate Hypotheses

• Clearly state what you’re trying to prove or disprove. Null Hypothesis (H0): No significant difference. Alternative Hypothesis (Ha): Expected difference based on research.

2. Select a Significance Level (α)

• Next, choose a small number (often 0.05 or 5%) as a cutoff for significance.
• The results are significant if the p-value (calculated later) is lower than α.

3. Collect Data

• Gather data relevant to your study from experiments or observations.
• Also, ensure data is accurate and representative of the population.

4. Choose a Statistical Test

• Pick the proper test based on your research question and data type.
• For instance, t-test for comparing two groups’ means, chi-square for categorical data, etc.

5. Calculate Test Statistic

• Now, use the chosen test formula to calculate a test statistic (like t, z, F).
• The formula varies for each test. You must refer to statistical resources or software.

6. Find P-Value

• You must have understood by now that the P-value indicates the probability of getting your results by chance.
• Moreover, a lower p-value suggests results are less likely due to chance.

7. Compare P-Value and α

• The results are statistically significant if the p-value is less than α (your chosen significance level).
• It means observed differences are likely, not random.

8. Draw Conclusions

• All things considered, it is vital to check the practical importance of your findings.
• If the results are statistically significant, they support your research hypothesis.
• However, be cautious. Statistical significance doesn’t always mean practical importance.

Limitations of Statistical Significance

• Sensitivity to Sample Size: Big samples can sometimes overemphasize minor differences. This makes them seem more important than they really are.
• Influence of Outliers:   Some results might seem more significant or less significant than they indeed are. Why? Because outlying extreme values can mess with our results.
• Cumulative Significance Testing: If we keep testing the exact same data for different things, we increase the risk of finding something significant just by chance.
• Contextual Disregard: Biases and misrepresentations are inevitable at times. These tests often consider the unique context of a situation.

The Role of Statistical Significance in Hypothesis Testing

Statistical significance acts as the verdict!

Accept or Reject Hypotheses: Statistical significance guides researchers to either accept their hypothesis (if the results are significant) or reject it (if not).

Informed Decisions: It ensures decisions are based on solid evidence. This reduces the risk of making conclusions without proper support.

Minimizes Errors: Researchers rely on significance levels to minimize the chance of Type I (false positive) and Type II (false negative) errors in their conclusions.

Scientific Validity: Significance ensures that scientific studies meet rigorous standards. The result? Credibility and reliability of research outcomes.

Guides Further Research: Positive significance motivates further exploration. You get deeper insights and expand the scope of knowledge.

As we’ve explored, statistical significance isn’t merely a checkbox to tick. It’s a nuanced understanding of probabilities and outcomes. It separates chance from genuine patterns, helping researchers discern the meaning from the coincidental. However, it’s vital to acknowledge its limitations and interpret results contextually.

So, the next time you encounter a research study or delve into statistical analyses, remember the significance of statistical significance. It’s not just numbers!

And while at it, why not take SurveySparrow for a spin? You can streamline your data collection process.

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Understanding the Interpretation of Results in Research

Doing the interpretation of results in research is crucial to obtaining valuable findings. Learn how to achieve a good interpretation here!

Research is a powerful tool for gaining insights into the world around us. Whether in academia, industry, or the public sector, research studies can inform decision-making, drive innovation, and improve our understanding of complex phenomena. However, the value of research lies not only in the data collected but also in the interpretation of results. Properly interpreting research findings is critical to extracting meaningful insights, drawing accurate conclusions, and informing future research directions. 

In this Mind the Graph article, you’ll understand the basic concept of interpretation of results in research. The article will go over the right procedure for checking, cleaning, and editing your data as well as how to organize it effectively to aid interpretation.

What is the interpretation of results in research?

The process of interpreting and making meaning of data produced in a research study is known as research result interpretation. It entails studying the data’s patterns, trends, and correlations in order to develop reliable findings and make meaningful conclusions.  

Interpretation is a crucial step in the research process as it helps researchers to determine the relevance of their results, relate them to existing knowledge, and shape subsequent research goals. A thorough interpretation of results in research may assist guarantee that the findings are legitimate and trustworthy and that they contribute to the development of knowledge in an area of study. 

The interpretation of results in research requires multiple steps, including checking, cleaning, and editing data to ensure its accuracy, and properly organizing it in order to simplify interpretation. To examine data and derive reliable findings, researchers must employ suitable statistical methods. They must additionally consider the larger ramifications of their results and how they apply to everyday scenarios. 

It’s crucial to keep in mind that coming to precise conclusions while generating meaningful inferences is an iterative process that needs thorough investigation. 

The process of checking, cleaning, and editing data

The process of data checking, cleaning, and editing may be separated into three stages: screening, diagnostic, and treatment . Each step has a distinct goal and set of tasks to verify the data’s accuracy and reliability. 

Screening phase

The screening process consists of a first inspection of the data to find any errors or anomalies. Running basic descriptive statistics, reviewing data distributions, and discovering missing values may all be part of this. This phase’s goal is to discover any concerns with the data that need to be investigated further.

Diagnostic phase

The diagnostic phase entails a more extensive review of the data to identify particular concerns that must be addressed. Identifying outliers, investigating relationships between variables, and spotting abnormalities in the data are all examples of this. This phase’s goal is to identify any problems with the data and propose suitable treatment options.

Treatment phase

The treatment phase entails taking action to resolve any difficulties found during the diagnostic phase. This may involve eliminating outliers, filling in missing values, transforming data, and editing data. This phase’s goal is to guarantee that the data is reliable, precise, and in the appropriate format for analysis.

Researchers may guarantee that their data is high-quality and acceptable for analysis by using a structured approach to data checking, cleaning, and editing.

How to organize data display and description?

Organizing data display and description is another critical stage in the process of analyzing study results. The format in which data is presented has a significant influence on how quickly it may be comprehended and interpreted. The following are some best practices for data display and description organization.

Best practices for qualitative data include the following:

• Use quotes and anecdotes: Use quotes and anecdotes from participants to illustrate key themes and patterns in the data.
• Group similar responses: Similar replies should be grouped together to find major themes and patterns in the data.
• Use tables: Tables to arrange and summarize major themes, categories, or subcategories revealed by the data.
• Use figures: Figures, such as charts or graphs, may help you visualize data and spot patterns or trends.
• Provide context: Explain the research project’s topic or hypothesis being examined, as well as any important background information, before presenting the findings.
• Use simple and direct language: To describe the data being given, use clear and succinct language.

Best practices for quantitative data include the following:

• Use relevant charts and graphs: Select the right chart or graph for the data being presented. A bar chart, for example, could be ideal for categorical data, but a scatter plot might be appropriate for continuous data.
• Label the axes and include a legend: Label the axes of the chart or graph and include a legend to explain any symbols or colors used. This makes it easier for readers to comprehend the information offered.
• Provide context: Give context to the data that is being given. This may include a brief summary of the research issue or hypothesis under consideration, as well as any pertinent background information.
• Use clear and succinct language: To describe the data being given, use clear and concise language. Avoid using technical jargon or complex language that readers may find difficult to grasp.
• Highlight significant findings: Highlight noteworthy findings in the provided data. Identifying any trends, patterns, or substantial disparities across groups is one example.
• Create a summary table: Provide a summary table that explains the data being provided. Key data such as means, medians, and standard deviations may be included.

3 Tips for interpretation of results in research

Here are some key tips to keep in mind when interpreting research results:  

• Keep your research question in mind: The most important piece of advice for interpreting the results is to keep your research question in mind. Your interpretation should be centered on addressing your research question, and all of your analysis should be directed in that direction.
• Consider alternate explanations: It’s critical to think about alternative explanations for your results. Ask yourself whether any other circumstances might be impacting your findings, and carefully assess them. This can assist guarantee that your interpretation is based on the evidence and not on assumptions or biases. 
• Contextualize the results: Put the results into perspective by comparing them to past research in the topic at hand. This can assist in identifying trends, patterns, or discrepancies that you may have missed otherwise, as well as providing a foundation for subsequent research. 

By following these three tips, you may assist guarantee that your interpretation of data is correct, useful, and relevant to your research topic and the larger context of your field of research.

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Statistical significance or clinical significance? A researcher's dilemma for appropriate interpretation of research results

Hunny sharma.

Department of Community and Family Medicine, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India

It is incredibly essential that the current clinicians and researchers remain updated with findings of current biomedical literature for evidence-based medicine. However, they come across many types of research that are nonreproducible and are even difficult to interpret clinically. Statistical and clinical significance is one such difficulty that clinicians and researchers face across many instances. In simpler terms, the P value tests all hypothesis about how the data were produced (model as whole), and not just the targeted hypothesis that it is intended to test (such as a null hypothesis) keeping in mind how reliable are the of the research results. Most of the times it is misinterpreted and misunderstood as a measure to judge the results as clinically significant. Hence this review aims to impart knowledge about “P” value and its importance in biostatistics, also highlights the importance of difference between statistical and clinical significance for appropriate interpretation of research results.

Introduction

Currently, in the publish or perish era where most of the researches are judged based on their statistically significant findings, it is often difficult for young researchers to interpret the correct findings of the research. The recent development of high-speed and more sophisticated computing power, utilizing high-end computers and statistical software packages, has resulted in a significant increase in the use of statistical methods, tests for hypothesis testing and reporting to the health literature. Unluckily, the appropriate interpretation of research results from the clinical point of not received similar interest.[ 1 ] This imbalance from decades to determine the actual importance of statistical and clinical significance and publication of such results in reputed indexed journals had led researchers to consider statistically significant results also as a clinically important one. It is essential to understand that publications in reputed indexed journals do not indicate that appropriate study design or statistics methods were used. This dilemma of the young researchers creates obstacles in their clinical decision-making and ultimately affects their role in Evidence-based practice.[ 2 ]

Researchers must realize that a clinical study is valuable and is of importance to clinical practice when the results are appropriately interpreted. Every year hundreds of studies and clinical trials are conducted to test different hypothesis. These trials are entirely dependent on appropriate statistical tests to assess whether new therapies or treatment protocol are better in clinical practice as compared to the usual approach or methods. Researchers should understand what is the importance of both statistical and clinical significance.[ 3 ]

When looking from a clinical point of view, the statistically significant difference among groups is not of prime importance. If a well-conducted study shows a difference in treatment options within two groups, it is of prime importance to know whether that difference is of clinically importance or not.[ 4 ] Since sample size and measurement variability can easily influence the statistical results, a nonsignificant outcome does not imply that the new therapy or treatment protocol is not clinically useful.[ 5 , 6 ]

Hence this review aims to impart knowledge about “ P ” value and its importance in biostatistics, also highlights the importance of difference between statistical and clinical significance for appropriate interpretation of research results.

What does P value infer?

In simpler terms, the P value tests all hypothesis about how the data were produced (the whole model), not just the targeted hypothesis that it is intended to test (such as a null hypothesis).[ 7 ]

The P value is the likelihood that if every model assumption, including the test hypothesis, were correct, the chosen test statistic would have been at least as large as its observed value.[ 7 ]

The most common threshold value for the “ P ” we find in biomedical literature is 0.05 (or 5%), and most often the P value is distorted into a dichotomous number where results are considered “statistically significant” when P falls on or below a cut-off (usually 0.05) and otherwise its declared “nonsignificant”.[ 7 ]

Why are “ P ” values not enough?

According to Ron Wasserstein, ASA's executive director, the P value was never meant to substitute the scientific reasoning, which is of greater interest. P value, which is a number whose value can range from zero to one in relation to a threshold value, represents the probability that the difference between the groups is not by chance. A well-reasoned and scientifically driven explanation will always remain the basis of reporting scientific outcomes.[ 8 ]

On what factors does the “ P ” value depend?

It should be borne in mind that the “ P ” value only represents that to what extent the data are inconsistent or incompatible with a given specific statistical model (i.e., null hypothesis). Hence it only aims to accept or reject the null hypothesis rather than focusing on the research hypothesis. From a statistical point of view, it measures the strength of evidence against the null hypothesis.[ 9 ]

With the advancement in biostatistics, it is now clear that the “ P ” value can easily be affected by various factors like sample size, the magnitude of the relationship and error. Each of these factors can work independently or in combination to distort the study findings based on “ P ” values.[ 10 ]

(1) Effect of error on “ P ” values

In general, two types of errors that is, systematic and random error effects the “ P ” value.

“Systematic errors,” that is, “Non-random errors” of certain significant magnitude distorts the research results towards a specific direction or can result in altered observed association in either direction. This type of error generally occurs when a single examiner takes the measurement leading to an unintended bias of deviating the research results to his/her expectations or may also result from modification of the measuring technique. Hence, Systematic error is a systematic flaw in the measurement of a variable due to methodological error leading to underestimation or overestimation of measurements. The extent of systematic errors can be determined by re-examination and re-measurement of a certain sufficient number (i.e., 20%, not always applicable) of individuals again by material and method used in the agreement. Some statistical tests like paired t -test, the intraclass correlation and the Bland-Altman method can also help in the determination of systematic errors.[ 10 , 11 , 12 ]

A “random error” is defined as a variability of the data which cannot be explained. Random errors of high magnitude means trouble in reproducibility of the measurements, which may result in questionable methodology and questionable examiners’ ability. This occurs randomly across the population, ultimately distorting the results. Random errors can be minimized by taking a large number of samples or measurements. Let us understand this by taking an example of measuring Mid-Upper Arm Circumference (MUAC) of the population. While measuring the MUAC of each individual in the population, random error may exhibit itself in the form of random MUAC among individuals that is, less or more MUAC measured as compared to the actual measurement. This may be a result of how the tape was held while taking the measurement, at what position it was measured (ideally midway between the olecranon process and the acromion), and who was the researcher who took the measurement. Random error can be reduced by incorporating a large number of samples or measurements that is, the more study participants are included in these measurements, the smaller the effect of random error will become.[ 10 ]

(2) Effect of sample size on “ P ” values

It is well known that the P value depends on the sample size to a vast extent. More the sample size less will be the variability of the measurement or data, and more precise will be the measurement for the study population. With an increase in sample size, the magnitude of random error decreases and the study is more likely to find a significant relationship if it exists.[ 10 ]

(3) Effect of magnitude of relationship between groups on “ P ” values

P -value also relies on the magnitude of difference or relationship between the groups compared. In simpler terms, if the magnitude of difference between the two groups is more substantial, then it will be easy to detect and will have a small P value.[ 10 ]

What are the American Statistical Association (ASA) principal statements on statistical significance and P values?

ASA on 8 th March 2016, in the event of the growing concern of misuse and misinterpretation of P values, gave six principal statements to improve conduct and interpretation of quantitative research and increase research reproducibility. The six principal statements issued regarding significance and P value which are as follows:

• P -value shows the extent of incompatibility of the data with the stated statistical model.[ 8 ]
• P -value is neither the measure of the probability of the studied hypothesis being true nor is the representation of the probability that study data were produced by random chance alone.[ 8 ]
• It is extremely important to note that any business model, policy decision, or conclusion related to any scientific study or experiment should not be based on the P value and merely on the fact whether it passes a specific threshold or not.[ 8 ]
• It is the moral duty of the authors and researchers to report the research or experimental findings to its full extent and with transparency.[ 8 ]
• A P value is neither represents the importance of research results nor is the representation of the effect size of the study.[ 8 ]
• P -value does not give a sufficient measure of evidence regarding a model or “hypothesis”.[ 8 ]

What are clinically significant outcomes?

The term “clinically significant” can be used for the researches in which clinically relevant results or outcomes are used to assess the effectiveness or efficacy of a treatment modality. When used the term “clinically significant” findings are those who make the patient improves the quality of life and makes him/her feel, function well.[ 13 ]

Clinically significant findings are those which improve medical care resulting in the improvement of individual's physical function, his/her mental status, and ability to engage in social life. The term improvement of quality of life in medical care deals with both subjective and objective terms. Here the term objective deals with improvement in performance status, duration of remission of disease, and prolongation of life-span, while subjective improvement in quality-of-life deals with improved mood, attitude, physical and social activity, feeling of general wellbeing, and the alleviation of distressing symptoms like pain, weakness, and discomfort.

Since statistical significance results do not necessarily mean that the results are clinically relevant and lead to improvement in the quality of life of the individuals. Hence, many outcomes can be statistically significant but not clinically relevant in a clinical point of view. Hence, clinicians and researchers should give importance to both statistical and clinical significance.[ 13 ]

A clinically relevant intervention justifies the effects which over-benefits the associated costs, harm, and the inconveniences caused to the individuals for whom it is targeted. The main difference between statistical and clinical significance is that the clinical significance observes dissimilarity between the two groups or the two treatment modalities, while statistical significance implies whether there is any mathematical significance to the carried analysis of the results or not.

Different studies can have a similar statistical significance but may differ significantly in clinical significance. Let's consider an example of two different chemotherapy agents for cancer. The first study estimates to increase the survival of treated patient with Drug A (Less Expensive than usual chemotherapeutic agents) by five years ( P = 0.01) and alpha being 0.005, similarly a Second study utilizing Drug B (Expensive than usual chemotherapeutic agents) estimates to increase the survival of treated patient by mere five months ( P = 0.01) and alpha being 0.005. In both cases, the statistical test is significant, but Drug B only increases the survival by only five months which is not clinically significant as compared to Drug A which increases survival by five years, nor useful in terms of cost-effectiveness and superiority when compared to already available chemotherapeutic agents.[ 14 , 15 ]

Hence from the above description of statistically significant and clinically significant results, it is clear that both the notations have the importance of their own. The statistically significant results may not of clinical importance, vice versa the results which are of clinical importance may not be statistically significant. It is high time now that the researchers, journal editors, and readers should take a keen interest in looking beyond the threshold “ P ” value and also consider the results from a clinical point of view rather than just assessing the worth of research by considering the “ P ” values. All the researchers should also take into account the design, sample size, effect size of the study, bias incorporated, and reproducibility of the study while analyzing the study results. An aware researcher with a logically and critically thinking mind is in the best position to evaluate research results and thereby applying them to practice evidence-based medicine. Logically, discussion of the clinically significant research results will increase discussion and understanding of the new treatment modalities and will help in the upliftment of evidence-based practice.

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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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Understanding research – what a significant result actually means

• in Blog , Evidence-Based Practice , Research by David Wilkinson

In this article –

Only the uninformed talk about research proving things

The significance of significance

How likely are we to get this result by random chance?

Levels of significance

What a significant result means

In my last blog about research we looked at the fact that it’s almost impossible to prove something. Even if you thought you had proved it quite likely a new method of research or a way of seeing things or new/better data will likely come along eventually and either change what we think about something or allow us to see it in a new and hopefully better way. So the best we can say about any research is that the findings are the best, most up-to-date findings that we have right now. We always have to be open to the idea that later research may add to, change or even completely overturn our understanding of the topic under consideration.

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This is why only the uninformed talk about research proving things. Researchers and academics understand that you can only show that something isn’t the case, for example that feeding your employees chocolate causes customers to buy more of your product. We accept a hypothesis based on the fact that the best evidence at the moment shows that the opposite, the null hypothesis, can’t be true (that feeding your employees chocolate does not increase product sales).

So research shows you the best evidence to support something at the moment or what theory to explain something best fits the available data right now.

The term significant to a researcher means how likely are we to have got this result by chance?

Say some researchers found that 50% of people in America believe in Santa Claus. Is that high or low or are you likely to get that result by random chance?

Ok, the first thing is that given the fact that there are somewhere over 318 million people in America, the researchers are very unlikely to have asked and got a response from every person in America. So they will have sampled a range of the population. The question is, given that it is a sample of the population, how likely are these results to occur just by chance, given the size of the sample and how representative that sample is of the population generally?

There are a series of statistical methods (which I won’t go into now) that can give us an answer to that. If, say, you only asked two people, then not only would this not represent the entire population of the US, but any result you get could easily happen due to random change. The results would be described as not significant. The result is not significant because if you asked just two people, the chances are that you would get almost any result anyway. It doesn’t really tell us anything. However if you sampled a large enough percentage of the population controlling for (or taking into consideration) the age, education, socio-economic status and other characteristics so that the sample truly represented the population of the US, then you might find that the chances of getting a result of 50% believers by random chance is less than 95%. In other words there is only a 5% likelihood that you could get this result by random chance. This result could then be described as significant. This doesn’t mean it is proved, it means that you are only 5% likely to get this result by random chance.

In fact 95% is the usual minimum level researchers use as evidence that their results are significant. There tends to be two cut off points for research, we are 95% sure that the results are what we think they are and 99%. These are called levels of significance and obviously being 99% sure the results are not as a result of random chance is rather better than being 95% sure.

In my next post I will look at how and when we can say something causes something else and what correlations are.

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Poverty of the stimulus and its role in learning the Chinese language according to syntactic content

• Published: 10 July 2024

Cite this article

• Zhang Xiaowan 1  

The notion of “poverty of stimulus” in the realms of linguistics and philosophy persists as a pertinent concern, given that language acquisition constitutes a pivotal facet of human culture and communication. The purpose of this study is to evaluate the proficiency of students in their endeavours to learn the Chinese language, focusing on syntactic attributes, under the influence of the concept of “poverty of the stimulus.” The study incorporates a cohort of 320 participants comprising students engaged in specialized Chinese language courses across three prominent Chinese universities. The sample was distributed as follows: 55 from the high proficiency group, 115 from the intermediate group, and 150 from the beginner level group. The mean age of the participants is approximately 28 years with a standard deviation of 1.73, 1.73, and 11.83 respectively. The findings illuminate substantial variability in outcomes among students about various factors, including academic achievement, practical application, cultural literacy, and educational attainment. Additionally, the standard errors of the mean for these factors are observed to be significant, underscoring the substantial heterogeneity in responses ( SEM = 0.0194 ; SEM = 0.0185; SEM = 0.0186; SEM = 0.0184). It is estimated that the factor of ‘Social Connection’ exhibits a relatively low standard deviation (SD = 0.2443) and a small standard error of the mean (SEM = 0.0136), indicating reduced variability in results and greater consensus in student responses concerning social connections, in comparison to other factors related to student development. It has been determined that ‘Social Connection’ emerges as the most significant motivational factor for students, exerting a decisive influence on their success in learning the Chinese language, particularly in its syntactic dimension. The findings of this study hold potential utility for educators in the field of philological disciplines, who are engaged in refining the pedagogical techniques for teaching the Chinese language and other foreign languages. Furthermore, this research remains pertinent to the students themselves who are contemplating the pursuit of Chinese or any other foreign language studies, aiding them in gaining a deeper understanding of their motivation and how to manage it effectively.

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Xiaowan, Z. Poverty of the stimulus and its role in learning the Chinese language according to syntactic content. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06297-7

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Accepted : 19 June 2024

Published : 10 July 2024

DOI : https://doi.org/10.1007/s12144-024-06297-7

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