Clinical Significance vs. Statistical Significance - Side-by-Side Comparison (2022)

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In extremely broad terms, statistical significance means that it’s likely that something is happening, while clinical significance verifies to what extent that thing is happening. Put another way: statistical significance seeks to disprove a negative, and say an event probably didn’t happen by chance; clinical significance seeks to prove a positive, and say an event did happen in a particular, measured manner.

(Video) statistical vs clinical significance

Unfortunately, it’s also a lot more complicated than that, and this goes beyond simple semantics: a crisis on the non-replicability of scientific studies in the last decade has caused vociferous arguing about what these terms mean, and how they’re decided, and how much importance they should convey.

There’s an informal fallacy that can help illustrate aspects of that crisis, and it’s called the Texan Sharpshooter Fallacy. In this thought exercise, a Texan starts shooting at the side of a barn. When he’s run out of bullets, he walks over to the barn and paints a giant target sign, making sure to draw the center of the target around the area where the most bullets landed. Hey look, he says, I’m a sharpshooter! From a statistically significant point of view, he’s right: it couldn’t possibly have happened by chance that all those shots landed so close to the bullseye.

Now, in the academic world, that type of misrepresentation is far less exaggerated, far less common, and practically never intentional. Still, certain forces have implicitly encouraged small errors in data collection and put a priority on the wrong kinds of significance.

To put it succinctly, some academic journals and studies placed far too much importance on statistical significance, which, it turns out, sometimes isn’t all that significant. If this sounds confusing, don’t worry; it’s confusing to some extremely smart people, too.

To learn the differences, similarities, and applications of clinical and statistical significance, check out our side-by-side comparison chart below.

Clinical SignificanceStatistical Significance


In medical terms, clinical significance (also known as practical significance) is assigned to a result where a course of treatment has had genuine and quantifiable effects. Broadly speaking, statistical significance is assigned to a result when an event is found to be unlikely to have occurred by chance.

Key Terms

  • The effect size is a measure of an observed phenomenon’s magnitude. This could include the correlation between two variables, the mean difference between two variables, or the risk of a particular event happening.
  • The number needed to treat (NNT) is a type of effect size that measures the average number of patients who need to be treated to prevent one additional bad outcome, or the number who need to be treated for one to benefit over the control.
  • The Jacobson-Truax approach calculates clinical significance through a Reliability Change Index (RCI), which is equal to the difference between a study participant’s pre-study and post-study scores, divided by a standard error of the difference. Participants are also scored into categories for deteriorated, unchanged, improved, and recovered.
  • The null hypothesis is the default assumption that there is no statistical significance: that nothing observed has changed, and/or there is no association or relationship between observed data sets.
  • The p-value is the probability of achieving a study’s results if the null hypothesis is assumed to be true. If P=0.038, for example, it means there’s a 3.8 percent chance that any observed difference was the result of random chance.
  • The significance level of a study is set in advance, before data is collected. Originally, it was defined as 0.05, or 5 percent, but it can be set much, much lower, depending on the field of study (particle physics or genomics, for example, may use up to nine decimal places). If, after all data is collected, The p-value is less than or equal to the significance level, the results are said to be statistically significant.


Historically, clinical significance has been applied primarily in pharmaceutical trials and medical research. It also has had major implications in psychology and psychotherapy, where the term clinical significance differs slightly in its criteria and formulation. There’s no simple equation for determining clinical significance, and numerous methods have developed over time for arriving at more accurate results.

In the 21st century, the emergence of big data and the field of data science have brought a renewed focus on the concept of clinical significance, which is often referred to as ‘practical significance’ when used outside of a medical research setting. Practical significance now allows researchers to dig deeply into data sets and draw conclusions with a high amount of confidence.

While the history of statistical significance dates back to the 18th century, it wasn’t until 1925 that British statistician and geneticist Ronald Fisher advanced the idea within statistical hypothetical testing. Fisher put forth the initial threshold of 0.05, or 5 percent, for determining statistical significance, but never intended it to be a standard cutoff point, instead recommending it be adjusted based on contextual characteristics.

(Video) clinical vs statistical significance 2022

By the early 2010s, academic journals began to note an alarming overuse of the 0.05 p-value, and an editorial bias towards publishing studies that met that threshold. Such bias led to practices such as ‘p-hacking’ that emphasized p-value over other forms of scientific and statistical reasoning.

In 2016, the American Statistical Association issued a statement saying that p-value should never be used as a substitute for scientific rigor, and that it hoped to steer research into a ‘post p<0.05 era’.


Clinical significance has key applications in vaccine testing, pharmaceutical testing, and other forms of medical research where the magnitude and specific implications of a particular intervention need to be measured and quantified. But it also has use in non-medical settings, too, where it can provide a more rigorous critique of a data set. Statistical significance has broad applications wherever one is looking to learn whether something happened by chance, including market research with A/B testing, and opinion research with surveys or polls. It can also be useful in the early stages of pharmaceutical testing to determine whether further research is warranted.


A pharmaceutical company is testing the efficacy of a new anti-pain drug with clinical trials. It cultivates a representative participant group, gauges their pre-test pain levels, and then closely monitors their progression, before recording their post-test pain levels. Participants are sorted into tiers of their level of effectiveness, and weighed against a control, with further contextual data taken into account (i.e., side-effects and pre-existing conditions). At the conclusion of the study, researchers will have a more informed sense of the new drug’s effect. A retail company is considering a new advertising campaign. It shows the new campaign to a sample group, and finds that they spend, on average, more than those who saw the old campaign. If the results are statistically significant, a correlation between the new ad and increased spend is likely, and the company may switch all their marketing material to the new campaign.


Clinically significant results are reproducible to a much higher degree than those which are merely statistically significant, and therefore can be used in scenarios with higher stakes, where even a modest margin of error would be too high. Clinical significance is a requirement when performing pharmaceutical testing, and the nuanced results that come with it can produce radical insights. Statistical significance helps scientists, companies, and other entities understand how strongly the results of an experiment, survey, or poll should influence their decisions. But it’s far from the only factor one should consider when assessing the importance of a particular result. Sample size, contextual characteristics, and more complete research must be taken into account as well.


(Video) Practical vs Statistical significance

The Bottom Line

Clinical significance seeks to understand the size and scope of an effect. It’s a critical tool for decision-makers who are dealing with high-stakes pharmaceutical, psychological, and medical research. However, the scientific rigor and reproducibility of this type of data analysis is also attractive to data scientists answering questions in other industries. Statistical significance seeks to verify that an effect is taking place. It can be a helpful tool for decision-makers when taking into account the results of a particular study. However, it should not be the primary determinant of truth, efficacy, or importance. A stricter focus on scientific rigor and smaller p-values will reduce the chances of false positives and irreproducibility in statistical research.
(Video) 108. Statistical vs. Clinical Significance | THUNK

Clinical Significance vs. Statistical Significance - Side-by-Side Comparison (1)

Matt Zbrog


Matt Zbrog is a writer and freelancer who has been living abroad since 2016. His nonfiction has been published by Euromaidan Press, Cirrus Gallery, and Our Thursday. Both his writing and his experience abroad are shaped by seeking out alternative lifestyles and counterculture movements, especially in developing nations. You can follow his travels through Eastern Europe and Central Asia on Instagram at @weirdviewmirror. He’s recently finished his second novel, and is in no hurry to publish it.

(Video) Clinical and Statistical Significance


Is it possible for a research study to have statistically significant results but limited clinical value? ›

A study outcome can be statistically significant, but not be clinically significant, and vice‐versa. Unfortunately, clinical significance is not well defined or understood, and many research consumers mistakenly relate statistically significant outcomes with clinical relevance.

How do you determine if there is a statistically significant difference between two claims? ›

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.

How do you determine if a study is clinically significant? ›

In health care research, it is generally agreed that we want there to be only a 5% or less probability that the treatment results, risk factor, or diagnostic results could be due to chance alone. When the p value is . 05 or less, we say that the results are statistically significant.

How do you compare statistical significance? ›

Steps in Testing for Statistical Significance
  • State the Research Hypothesis.
  • State the Null Hypothesis.
  • Select a probability of error level (alpha level)
  • Select and compute the test for statistical significance.
  • Interpret the results.

What is the difference between statistically significant and clinically significant? ›

In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on clinical practice.

Which is more important clinical significance or statistical significance? ›

Clinically significant results are reproducible to a much higher degree than those which are merely statistically significant, and therefore can be used in scenarios with higher stakes, where even a modest margin of error would be too high.

How do you compare two samples with different sizes? ›

One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set.

How many samples do I need to be statistically significant? ›

Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them.

How do you interpret a significant difference? ›

In principle, a statistically significant result (usually a difference) is a result that's not attributed to chance. More technically, it means that if the Null Hypothesis is true (which means there really is no difference), there's a low probability of getting a result that large or larger.

What is considered clinically significant? ›

So, in simple terms, if a treatment makes a positive and noticeable improvement to a patient, we can call this 'clinically significant' (or clinically important). In contrast, statistical significance is ruled by the p-value (and confidence intervals).

How do you know if a change is clinically significant? ›

Clinically significant change is is change that has taken the person from a score typical of a problematic, dysfunctional, patient, client or user group to a score typical of the "normal" population.

How do you interpret P values in clinical trials? ›

The P value obtained from the data is judged against the α. If α=0.05 and P=0.03, then statistical significance is achieved. If α=0.01 and P=0.03, statistical significance is not achieved.

How do you know if data is statistically significant? ›

Researchers use a measurement known as the p-value to determine statistical significance: if the p-value falls below the significance level, then the result is statistically significant. The p-value is a function of the means and standard deviations of the data samples.

What response rate is statistically significant? ›

As a very rough rule of thumb, 200 responses will provide fairly good survey accuracy under most assumptions and parameters of a survey project. 100 responses are probably needed even for marginally acceptable accuracy.

What is an example of statistical significance? ›

Testing your hypothesis

Statistical significance is most practically used in hypothesis testing. For example, you want to know whether changing the color of a button on your website from red to green will result in more people clicking on it.

What is statistical significance in healthcare? ›

Statistical significance refers to whether any differences observed between groups being studied are "real" or whether they are simply due to chance. These can be groups of workers who took part in a workplace health and safety intervention or groups of patients participating in a clinical trial.

Why do we prefer to set the α value at .05 or .01 rather than some other number? ›

Popular Answers (1)

Reducing the alpha level from 0.05 to 0.01 reduces the chance of a false positive (called a Type I error) but it also makes it harder to detect differences with a t-test. Any significant results you might obtain would therefore be more trustworthy but there would probably be less of them.

How can you use clinical significance to support positive outcomes in your project? ›

I can use clinical significance to support positive outcomes in my project outcome by ensuring that the result is statistically significant. This is due to the fact that majority of statistically significant findings are normally have clinical significance.

Why is clinical significance evaluated? ›

The evaluation of research findings is crucial to help clinical decision making and to comply with the principles of evidence based-practice. Statistical significance testing has dominated the way researchers typically report their results and evaluate their significance.

What does abnormal result clinically significant mean? ›

There will be 64% with at least one abnormal result (Box 1). However, the more abnormal the result and the more related tests are abnormal, the more likely the abnormality is clinically significant. If you consider the 99% reference range (approx. ± 2.6 standard deviations) and the 99.9% reference range (approx.

What is the best way to compare two sets of data? ›

Common graphical displays (e.g., dotplots, boxplots, stemplots, bar charts) can be effective tools for comparing data from two or more data sets.

What type of t-test should be used to compare the two methods? ›

The two-sample t-test is used to compare the means of two different samples.

How do you compare two distributions in statistics? ›

The simplest way to compare two distributions is via the Z-test. The error in the mean is calculated by dividing the dispersion by the square root of the number of data points. In the above diagram, there is some population mean that is the true intrinsic mean value for that population.

How do you know if a sample size is large enough? ›

Large Enough Sample Condition
  1. You have a symmetric distribution or unimodal distribution without outliers: a sample size of 15 is “large enough.”
  2. You have a moderately skewed distribution, that's unimodal without outliers; If your sample size is between 16 and 40, it's “large enough.”

Is a sample size of 30 statistically significant? ›

Key Takeaways. The central limit theorem (CLT) states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's distribution. Sample sizes equal to or greater than 30 are often considered sufficient for the CLT to hold.

Is 30 respondents enough for a survey? ›

Academia tells us that 30 seems to be an ideal sample size for the most comprehensive view of an issue, but studies with as few as 10 participants can yield fruitful and applicable results (recruiting excellence is even more important here!).

What does it mean if results are not statistically significant? ›

This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).

How much statistical significance do you need to feel confident in regression results? ›

In regression analysis and hypothesis testing, we analyze and compare our results at a certain level of significance. The general rule of thumb in the field of statistics is to make use of an α=. 05. level of significance.

What does questionable clinical significance mean? ›

Uncertain clinical significance means the brain image shows something unusual in the brain, but we do not know if/how it may affect your child's health. Treatment may not be appropriate or possible.

What is the clinical value? ›

The Clinical Value Compass, named to reflect its similarity in layout to a directional compass, has at its four cardinal points (1) functional status, risk status, and well-being; (2) costs; (3) satisfaction with health care and perceived benefit; and (4) clinical outcomes.

What is clinically significant distress? ›

From the paragraph noted above (referring to a symptom threshold and to eliminating milder symptom presentations), it seems clear that “clinically significant” simply means a clinician's judgment that the distress or impairment is significant, marked, or substantial in intensity or duration.

How does SPSS calculate RCI? ›

A Reliable Change Index (RCI) is computed by dividing the difference between the pretreatment and posttreatment scores by the standard error of the difference between the two scores.

What is a clinically meaningful change? ›

Meaningful change can be defined as a change that has clinical or practical importance, has an impact on an individual's self-perceived health status or quality of life, or as a fraction of the standard deviation representing a certain level of movement across the distribution of measurements in the population.

What is RCI in statistics? ›

Definition. Reliable Change Index (RCI) is a concept in measurement and assessment. An RCI is a psychometric criterion used to evaluate whether a change over time of an individual score (i.e., the difference score between two measurements in time) is considered statistically significant.

Is p-value of 0.95 significant? ›

A p-value >0.95 literally means that we have a >95% chance of finding a result less close to expectation and, consequently, a <5% chance of finding a result this close or closer. Often in studies a statistical power of 80% is agreed upon, corresponding with a p-value of approximately 0.01.

Is p-value of 0.001 significant? ›

Conventionally, p < 0.05 is referred as statistically significant and p < 0.001 as statistically highly significant.

Is p-value 0.1 significant? ›

For example, a p-value that is more than 0.05 is considered statistically significant while a figure that is less than 0.01 is viewed as highly statistically significant.

What does it mean when a study is statistically significant but not clinically meaningful? ›

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.

How can the results of a study be statistically significant but not meaningful? ›

During researches, results can be statistically significant but not meaningful. The situations occurs at the end of a study when the statistical figures relating to certain topics of study are calculated in absence of qualitative aspect and other details that can be used to make decisions (Munro, 2005).

When findings are statistically significant they are at a level of and the results are not due to? ›

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.

What does it mean if the results of a study are found to be statistically significant? ›

A study result is statistically significant if the p-value of the data analysis is less than the prespecified alpha (significance level). In our example, the p-value is 0.02, which is less than the pre-specified alpha of 0.05, so the researcher concludes there is statistical significance for the study.

What does it mean when something is clinically significant? ›

Clinical significance is the practical importance of an effect (e.g. a reduction in symptoms); whether it has a real genuine, palpable, noticeable effect on daily life.

How do you know if a change is clinically significant? ›

Clinically significant change is is change that has taken the person from a score typical of a problematic, dysfunctional, patient, client or user group to a score typical of the "normal" population.

What is clinical significance example? ›

In clinical trials, the clinical significance (“treatment effects”) is how well a treatment is working. For example, a drug might be said to have a high clinical significance if it is having a positive, measurable effect on a person's daily activities. Image: Virginia Tech.

Can you have statistical significance and not practical significance? ›

This simply means that some effect exists, but it does not necessarily mean that the effect is actually practical in the real world. Results can be statistically significant without being practically significant.

Can a treatment have statistical significance but not practical significance? ›

Practical significance is related to whether common sense suggests that the treatment makes enough of a difference to justify its use. It is not possible for a treatment to have statistical significance, but not practical significance.

What does it mean if your results are not statistically significant? ›

This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).

How do you tell if your results are statistically significant? ›

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

How many samples do I need to be statistically significant? ›

Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them.

What is the most common standard for statistical significance? ›

The most common level, used to mean something is good enough to be believed, is . 95. This means that the finding has a 95% chance of being true.

How many data points do I need for statistical significance? ›

A minimum of 30 observations is sufficient to conduct significant statistics.” This is open to many interpretations of which the most fallible one is that the sample size of 30 is enough to trust your confidence interval.


1. Statistical Versus Clinical Significance - Kuba Glazek, Ph.D.
(Methodology Related Presentations - TCSPP)
2. Statistical Significance versus Practical Significance
3. CTMC 2020 Webinar Series: From Statistical Significance to Clinical Significance
4. Clinical significance
(Elizabeth Lynch)
5. Sci Inq 101: Minimal Clinically Important Difference (MCID)
(Sci Inq 101)
6. Null Hypothesis, p-Value, Statistical Significance, Type 1 Error and Type 2 Error
(Stomp On Step 1)

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