{"id":4321,"date":"2026-05-16T12:28:36","date_gmt":"2026-05-16T06:58:36","guid":{"rendered":"https:\/\/www.editage.com\/insights\/an-introduction-to-non-parametric-tests-for-biomedical-researchers\/"},"modified":"2026-05-11T21:55:56","modified_gmt":"2026-05-11T16:25:56","slug":"an-introduction-to-non-parametric-tests-for-biomedical-researchers","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/an-introduction-to-non-parametric-tests-for-biomedical-researchers","title":{"rendered":"Normality tests, parametric tests, and non-parametric tests: Uses and assumptions"},"content":{"rendered":"<p>In this article, you\u2019ll learn<\/p>\n<ul>\n<li><a href=\"#_Toc229428530\">What is normality?<\/a><\/li>\n<li><a href=\"#_Toc229428531\">What are parametric tests?<\/a><\/li>\n<li><a href=\"#_Toc229428532\">What are the best parametric tests?<\/a><\/li>\n<li><a href=\"#_Toc229428533\">When can parametric tests be used?<\/a><\/li>\n<li><a href=\"#_Toc229428534\">What are non-parametric tests<\/a><\/li>\n<li><a href=\"#_Toc229428535\">Why should researchers use non-parametric tests?<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><a name=\"_Toc229428530\"><\/a>What is normality?<\/h2>\n<p>Normality in data means that the data looks like a bell-shaped curve when it is plotted in a histogram. For example, if you\u2019ve a sample of 4000 patients at a hospital and are measuring blood pressure,<\/p>\n<ul>\n<li>Most patients cluster around the middle (around 120\/80 mmHg)<\/li>\n<li>Fewer and fewer patients exist at the extremes (dangerously low or dangerously high)<\/li>\n<li>It\u2019s symmetrical (the left side mirrors the right)<\/li>\n<\/ul>\n<figure id=\"attachment_46878\" aria-describedby=\"caption-attachment-46878\" style=\"width: 300px\" class=\"wp-caption alignnone\"><img fetchpriority=\"high\" decoding=\"async\" class=\"size-medium wp-image-46878\" src=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/normal-distribution-of-blood-pressure-300x180.png\" alt=\"A histogram and bell curve showing normal distribution of blood pressure\" width=\"300\" height=\"180\" srcset=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/normal-distribution-of-blood-pressure-300x180.png 300w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/normal-distribution-of-blood-pressure-768x461.png 768w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/normal-distribution-of-blood-pressure.png 1000w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-46878\" class=\"wp-caption-text\">Example of a normal distribution<\/figcaption><\/figure>\n<h3>What is skewness?<\/h3>\n<p>Skewness is about asymmetry. A normal bell curve is perfectly symmetrical, but skewness measures how much your data leans to one side. If most blood pressure readings are normal but a few patients have extremely high readings, the graph gets a long tail pulling to the right. That\u2019s positive skew. If the graph leans left, you have a negative skew.<\/p>\n<figure id=\"attachment_46879\" aria-describedby=\"caption-attachment-46879\" style=\"width: 300px\" class=\"wp-caption alignnone\"><img decoding=\"async\" class=\"size-medium wp-image-46879\" src=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure-300x113.png\" alt=\"Two examples of skewed distribution\" width=\"300\" height=\"113\" srcset=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure-300x113.png 300w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure-1024x384.png 1024w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure-768x288.png 768w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure-1536x576.png 1536w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/skewed-distribution-of-blood-pressure.png 1600w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-46879\" class=\"wp-caption-text\">Examples of skewed distribution<\/figcaption><\/figure>\n<h3>What is kurtosis?<\/h3>\n<p>Kurtosis is about how pointy or flat your bell curve is. High kurtosis means data is tightly packed around the average with extreme outliers. Low kurtosis means it\u2019s more spread out and flat. Think of it as measuring whether your curve is a sharp mountain or a gentle hill.<\/p>\n<figure id=\"attachment_46880\" aria-describedby=\"caption-attachment-46880\" style=\"width: 300px\" class=\"wp-caption alignnone\"><img decoding=\"async\" class=\"size-medium wp-image-46880\" src=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration-300x113.png\" alt=\"Examples of kurtosis\" width=\"300\" height=\"113\" srcset=\"https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration-300x113.png 300w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration-1024x384.png 1024w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration-768x288.png 768w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration-1536x576.png 1536w, https:\/\/www.editage.com\/insights\/wp-content\/uploads\/2023\/09\/kurtosis-illustration.png 1600w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-46880\" class=\"wp-caption-text\">Examples of low (left) and high (right) kurtosis<\/figcaption><\/figure>\n<h3>Why should researchers check for normality?<\/h3>\n<p>Researchers need to check for normality before conducting many statistical tests because these tests <em>require<\/em> normality as a fundamental assumption. In other words, if you run such a test (called a parametric test) on data that isn\u2019t normally distributed, you\u2019ll end up with incorrect results.<\/p>\n<h3>Which are the best tests for normality?<\/h3>\n<p>There are a number of ways to check normality, explained in the table below:<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Test<\/strong><\/td>\n<td><strong>Best Used When<\/strong><\/td>\n<td><strong>Limitation<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Shapiro-Wilk<\/strong><\/td>\n<td>Small to medium samples (n &lt; 2,000): most powerful and widely recommended<\/td>\n<td>Overly sensitive with very large samples<\/td>\n<\/tr>\n<tr>\n<td><strong>Kolmogorov-Smirnov<\/strong><\/td>\n<td>Large samples; comparing data to a known distribution<\/td>\n<td>Weak with small samples; less sensitive than Shapiro-Wilk<\/td>\n<\/tr>\n<tr>\n<td><strong>Anderson-Darling<\/strong><\/td>\n<td>When you want to emphasize the tails of the distribution (important in medical data)<\/td>\n<td>Less commonly supported in basic statistical software<\/td>\n<\/tr>\n<tr>\n<td><strong>Q-Q Plot<\/strong><\/td>\n<td>Quick visual check at any sample size<\/td>\n<td>Subjective and relies on human interpretation<\/td>\n<\/tr>\n<tr>\n<td><strong>D\u2019Agostino-Pearson<\/strong><\/td>\n<td>Medium to large samples; checks skewness and kurtosis together<\/td>\n<td>Needs a sample size of more than 20 to be reliable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>While the easiest way to check for normality is to simply plot a histogram of your data and check its shape, this is pretty subjective.<\/p>\n<h2><a name=\"_Toc229428531\"><\/a>What are parametric tests?<\/h2>\n<p>Parametric tests are statistical tests that rely on the assumption that your data are normally distributed. If your data are <em>not <\/em>normally distributed, these tests will produce unreliable results.<\/p>\n<h2><a name=\"_Toc229428532\"><\/a>What are the best parametric tests?<\/h2>\n<p>The most popular parametric tests are as follows:<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Test<\/strong><\/td>\n<td><strong>What it does<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>t-test<\/strong><\/td>\n<td>Compares means between 2 groups<\/td>\n<\/tr>\n<tr>\n<td><strong>ANOVA<\/strong><\/td>\n<td>Compares means across 3+ groups<\/td>\n<\/tr>\n<tr>\n<td><strong>Pearson correlation<\/strong><\/td>\n<td>Measures linear relationship between two variables<\/td>\n<\/tr>\n<tr>\n<td><strong>Linear regression<\/strong><\/td>\n<td>Predicts one variable from another<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2><a name=\"_Toc229428533\"><\/a>When can parametric tests be used?<\/h2>\n<p>Parametric tests can be used if you have run a test for normality and confirmed that your data are normally distributed. Make sure that <em>all <\/em>the concerned variables are normally distributed. For example, if you\u2019re examining the correlation between age and triglyceride level, but age is not normally distributed, you can\u2019t run a Pearson\u2019s correlation analysis.<\/p>\n<p>&nbsp;<\/p>\n<h2><a name=\"_Toc229428534\"><\/a>What are non-parametric tests<\/h2>\n<p>Non-parametric tests are those that don\u2019t require your data to be normally distributed. Most of these tests work by ranking the data rather than using raw values; that\u2019s how they sidestep the normality assumption. They work just fine even if your data is heavily skewed or if you have ordinal (ranked) data.<\/p>\n<p>Which are the best non-parametric tests?<\/p>\n<p>The most popular non-parametric tests are as follows:<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Non-Parametric Test<\/strong><\/td>\n<td><strong>What It Does<\/strong><\/td>\n<td><strong>When to Use It<\/strong><\/td>\n<td><strong>Parametric Equivalent<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Mann-Whitney U<\/strong><\/td>\n<td>Compares distributions between 2 independent groups<\/td>\n<td>2 groups, non-normal data, ordinal\/continuous outcome<\/td>\n<td>Independent t-test<\/td>\n<\/tr>\n<tr>\n<td><strong>Wilcoxon Signed-Rank<\/strong><\/td>\n<td>Compares two related\/paired measurements<\/td>\n<td>Before-after designs, matched pairs<\/td>\n<td>Paired t-test<\/td>\n<\/tr>\n<tr>\n<td><strong>Kruskal-Wallis<\/strong><\/td>\n<td>Compares distributions across 3+ independent groups<\/td>\n<td>3+ groups, non-normal data<\/td>\n<td>One-way ANOVA<\/td>\n<\/tr>\n<tr>\n<td><strong>Friedman Test<\/strong><\/td>\n<td>Compares 3+ repeated measurements on the same subjects<\/td>\n<td>Repeated measures, non-normal data<\/td>\n<td>Repeated-measures ANOVA<\/td>\n<\/tr>\n<tr>\n<td><strong>Spearman Correlation<\/strong><\/td>\n<td>Measures monotonic relationship between two variables<\/td>\n<td>Ordinal data or non-linear relationships<\/td>\n<td>Pearson Correlation<\/td>\n<\/tr>\n<tr>\n<td><strong>Chi-Square Test<\/strong><\/td>\n<td>Tests association between two categorical variables<\/td>\n<td>Frequency\/count data, no normality needed<\/td>\n<td>\u2014 (no direct equivalent)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h2><a name=\"_Toc229428535\"><\/a>Why should researchers use non-parametric tests?<\/h2>\n<p>Non-parametric tests are your safety net. They are less powerful than parametric tests when assumptions are met, but far more trustworthy when they aren\u2019t.<\/p>\n<p>Here are some situations in which you should use non-parametric tests:<\/p>\n<ul>\n<li><strong>Your data isn\u2019t normally distributed<\/strong>: if your data is heavily skewed or has extreme outliers, the bell-curve assumption breaks down, making parametric tests unreliable.<\/li>\n<li><strong>Your sample size is small<\/strong>: with very few observations (e.g., n &lt; 30), you can\u2019t confidently verify normality, so non-parametric tests are a safer choice.<\/li>\n<li><strong>Your data is ordinal<\/strong>: if you\u2019re working with ranked or rating-scale data (e.g. \u201crate your satisfaction 1\u20135\u201d), calculating a mean doesn\u2019t really make sense, but ranks do.<\/li>\n<li><strong>Your data is categorical<\/strong>: for count or frequency data (e.g. \u201chow many students passed vs. failed\u201d), non-parametric tests like Chi-Square are the natural fit.<\/li>\n<li><strong>You have outliers you can\u2019t remove<\/strong>: since non-parametric tests use ranks instead of raw values, a single extreme value won\u2019t distort your results.<\/li>\n<li><strong>You\u2019re measuring subjective responses<\/strong>: surveys, Likert scales, and opinion data rarely meet parametric assumptions, making non-parametric tests more honest and appropriate.<\/li>\n<li><strong>Your variances are unequal across groups<\/strong>: parametric tests like ANOVA assume similar spread in each group; non-parametric tests don\u2019t require this.<\/li>\n<li><strong>You want to be cautious<\/strong>: when in doubt, non-parametric tests make fewer assumptions, so your conclusions are on safer statistical ground.<\/li>\n<\/ul>\n<p><em>This article was initially published on September 4, 2023, and revised on May 16, 2026.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this article, you\u2019ll learn What is normality? What are parametric tests? What are the best parametric tests? When can parametric tests be used? What are non-parametric tests Why should researchers use non-parametric tests? &nbsp; What is normality? Normality in data means that the data looks like a bell-shaped curve when it is plotted in [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":46882,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2420],"tags":[2622,288,1319,2778],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-4321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","tag-analysisofdata","tag-data-management","tag-statistical-analysis","tag-statistical-analysis-and-review"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to use normality tests, parametric tests, and non-parametric tests | Editage Insights<\/title>\n<meta name=\"description\" content=\"Learn what is normality, skewness, kurtosis, and how and when to use parametric and non-parametric tests like t-test.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/insights\/an-introduction-to-non-parametric-tests-for-biomedical-researchers\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"By following a style guide, authors can make their work easier to read and understand. 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