{"id":436,"date":"2023-03-21T10:53:32","date_gmt":"2023-03-21T10:53:32","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=436"},"modified":"2026-09-12T21:40:43","modified_gmt":"2026-09-12T16:10:43","slug":"hypothesis-testing-nhst","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/","title":{"rendered":"Hypothesis Testing and NHST: Definition, Steps, Tips, Examples"},"content":{"rendered":"<p>Hypothesis testing is a statistical method used to determine whether there is enough evidence in sample data to draw conclusions about a population. Instead of collecting data from an entire population, you take a sample and test whether the evidence supports or contradicts an assumption about that population.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_Is_Null_Hypothesis_Significance_Testing_NHST\" >What Is Null Hypothesis Significance Testing (NHST)?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Core_Concepts_and_Key_Terms\" >Core Concepts and Key Terms<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_are_Null_and_Alternative_Hypotheses\" >What are Null and Alternative Hypotheses?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#P_value\" >P value<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Significance_Level_%CE%B1\" >Significance Level (\u03b1)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#One-Tailed_vs_Two-Tailed_Tests\" >One-Tailed vs. Two-Tailed Tests<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Example_1\" >Example 1<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Example_2\" >Example 2<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Types_of_Statistical_Tests_in_NHST\" >Types of Statistical Tests in NHST<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Step-by-Step_Guide_to_NHST\" >Step-by-Step Guide to NHST<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_are_Type_I_and_Type_II_Errors\" >What are Type I and Type II Errors?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Limitations_of_Null_Hypothesis_Significance_Testing\" >Limitations of Null Hypothesis Significance Testing<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Best_practices_to_mitigate_these_limitations\" >Best practices to mitigate these limitations:<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Frequently_Asked_Questions_FAQs\" >Frequently Asked Questions (FAQs)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_is_statistical_power_and_why_does_it_matter\" >What is statistical power, and why does it matter?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#How_is_a_confidence_interval_related_to_a_hypothesis_test\" >How is a confidence interval related to a hypothesis test?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_does_it_mean_to_%E2%80%9Cpre-register%E2%80%9D_a_study\" >What does it mean to &#8220;pre-register&#8221; a study?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#When_should_I_use_a_non-parametric_test_instead_of_a_t-test_or_ANOVA\" >When should I use a non-parametric test instead of a t-test or ANOVA?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#What_is_the_difference_between_a_one-sample_and_two-sample_test\" >What is the difference between a one-sample and two-sample test?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/#Can_hypothesis_testing_be_used_with_observational_data_or_only_with_experiments\" >Can hypothesis testing be used with observational data, or only with experiments?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_Is_Null_Hypothesis_Significance_Testing_NHST\"><\/span>What Is Null Hypothesis Significance Testing (NHST)?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Null Hypothesis Significance Testing (NHST) refers to one particular procedure: you begin by assuming the null hypothesis is true, collect data, compute a test statistic, and then ask how probable your observed result would be under that assumption.<\/p>\n<p>The word <em>null<\/em> is key. It does not mean &#8220;zero&#8221;. It means the hypothesis of <em>no effect, no difference, no relationship<\/em>. Everything in NHST is organised around building a case against this default position.<\/p>\n<p>In the social and biomedical sciences, we use NHST to ask things like:<\/p>\n<ul>\n<li>Does a new antidepressant actually reduce symptoms more than a placebo?<\/li>\n<li>Do boys and girls differ in academic self-efficacy?<\/li>\n<li>Does social isolation increase mortality risk?<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Core_Concepts_and_Key_Terms\"><\/span>Core Concepts and Key Terms<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"What_are_Null_and_Alternative_Hypotheses\"><\/span>What are Null and Alternative Hypotheses?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The null hypothesis (H\u2080) is a statement of &#8220;no difference,&#8221; &#8220;no association,&#8221; or &#8220;no treatment effect.&#8221; The alternative hypothesis (H\u2090) is a statement of &#8220;difference,&#8221; &#8220;association,&#8221; or &#8220;treatment effect.&#8221; H\u2080 is assumed to be true until proven otherwise. However, H\u2090 is the hypothesis the researcher hopes to bolster.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"P_value\"><\/span>P value<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The P value answers the question: &#8220;If the null hypothesis were true, what is the probability of observing the current data or data that is more extreme?&#8221; Note that the P value is NOT the probability that the hypothesis (or any other hypothesis) is right or wrong.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Significance_Level_%CE%B1\"><\/span>Significance Level (\u03b1)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The significance level (\u03b1) represents how sure we want to be before saying the claim is false. Usually, we choose 0.05 (5%). Choosing \u03b1 = 0.05 means accepting a 5% chance of wrongly rejecting a true null hypothesis, i.e., a false alarm. <a href=\"https:\/\/www.geeksforgeeks.org\/data-science\/understanding-hypothesis-testing\/\">\u00a0<\/a><\/p>\n<p>In biomedical contexts where a wrong decision could harm patients, researchers often set \u03b1 = 0.01 or even 0.001.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"One-Tailed_vs_Two-Tailed_Tests\"><\/span>One-Tailed vs. Two-Tailed Tests<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A one-tailed test is used when we expect a change in only one direction: either up or down, but not both. A two-tailed test is used when we want to see if there is a difference in either direction, higher or lower.<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Test Type<\/strong><\/td>\n<td><strong>When to Use<\/strong><\/td>\n<td><strong>Example Hypothesis<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Right-tailed<\/strong><\/td>\n<td>Expecting an increase<\/td>\n<td>H\u2081: \u03bc &gt; 50<\/td>\n<\/tr>\n<tr>\n<td><strong>Left-tailed<\/strong><\/td>\n<td>Expecting a decrease<\/td>\n<td>H\u2081: \u03bc &lt; 50<\/td>\n<\/tr>\n<tr>\n<td><strong>Two-tailed<\/strong><\/td>\n<td>Any difference, direction unknown<\/td>\n<td>H\u2081: \u03bc \u2260 50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><span class=\"ez-toc-section\" id=\"Example_1\"><\/span>Example 1<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A sociologist testing whether immigrants score <em>differently<\/em> (not just higher or lower) on a civic knowledge test compared to native-born citizens would use a two-tailed test, since the direction of difference is theoretically uncertain.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Example_2\"><\/span>Example 2<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A pharmacologist testing whether a new antihypertensive <em>lowers<\/em> blood pressure (not raises it) would use a one-tailed (left-tailed) test.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Types_of_Statistical_Tests_in_NHST\"><\/span>Types of Statistical Tests in NHST<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Choosing the wrong test is one of the most common errors in applied research. The decision depends on the type of data (continuous vs. categorical), the number of groups, and whether population variance is known.<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Test<\/strong><\/td>\n<td><strong>Data Type<\/strong><\/td>\n<td><strong>Groups<\/strong><\/td>\n<td><strong>When to Use<\/strong><\/td>\n<td><strong>Example<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Z-test<\/strong><\/td>\n<td>Continuous<\/td>\n<td>1 or 2<\/td>\n<td>Large sample (n &gt; 30), known population SD<\/td>\n<td>Comparing national exam mean to a known standard<\/td>\n<\/tr>\n<tr>\n<td><strong><a href=\"https:\/\/www.editage.com\/blog\/t-test-definition-assumptions-formula-calculation\/#One-Sample_T-Test_Formula\">One-sample t-test<\/a><\/strong><\/td>\n<td>Continuous<\/td>\n<td>1<\/td>\n<td>Small sample, unknown population SD<\/td>\n<td>Testing if a clinic&#8217;s mean wait time differs from 30 min<\/td>\n<\/tr>\n<tr>\n<td><strong><a href=\"https:\/\/www.editage.com\/blog\/t-test-definition-assumptions-formula-calculation\/#Independent_Samples_T-Test_Formula_Equal_Variances_Assumed\">Independent samples t-test<\/a><\/strong><\/td>\n<td>Continuous<\/td>\n<td>2<\/td>\n<td>Comparing means of two unrelated groups<\/td>\n<td>Depression scores in therapy group vs. control<\/td>\n<\/tr>\n<tr>\n<td><strong><a href=\"https:\/\/www.editage.com\/blog\/t-test-definition-assumptions-formula-calculation\/#Paired_Samples_T-Test_Formula\">Paired t-test<\/a><\/strong><\/td>\n<td>Continuous<\/td>\n<td>2 (related)<\/td>\n<td>Same subjects measured twice<\/td>\n<td>Blood pressure before vs. after drug<\/td>\n<\/tr>\n<tr>\n<td><strong><a href=\"https:\/\/www.editage.com\/blog\/chi-square-test-types-explained-for-biomedical-researchers\/\">Chi-square test<\/a><\/strong><\/td>\n<td>Categorical<\/td>\n<td>2+<\/td>\n<td>Association between categorical variables<\/td>\n<td>Gender vs. vaccine hesitancy (Yes\/No)<\/td>\n<\/tr>\n<tr>\n<td><strong><a href=\"https:\/\/www.editage.com\/blog\/anova-types-uses-assumptions-a-quick-guide-for-biomedical-researchers\/\">ANOVA<\/a><\/strong><\/td>\n<td>Continuous<\/td>\n<td>3+<\/td>\n<td>Comparing means of \u22653 groups<\/td>\n<td>Anxiety scores across 3 therapy modalities<\/td>\n<\/tr>\n<tr>\n<td><strong>One-tailed tests<\/strong><\/td>\n<td>Any<\/td>\n<td>Any<\/td>\n<td>Directional hypothesis is pre-specified<\/td>\n<td>New drug expected to reduce tumour size<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Step-by-Step_Guide_to_NHST\"><\/span>Step-by-Step Guide to NHST<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Step 1: State the hypotheses.<\/strong> Define H\u2080 and H\u2081 in precise, testable terms before looking at the data.<\/li>\n<li><strong>Step 2: Choose the significance level (\u03b1).<\/strong> Pre-specify \u03b1, usually 0.05. Changing it after seeing results invalidates the test.<\/li>\n<li><strong>Step 3: Select the appropriate statistical test.<\/strong> Match the test to your data structure (see table above).<\/li>\n<li><strong>Step 4: Collect and organize the data.<\/strong> Gather a representative sample. Poor data quality produces misleading p values regardless of the test.<\/li>\n<li><strong>Step 5: Compute the test statistic.<\/strong> Calculate how far your sample result lies from what H\u2080 predicts, in units of standard error.<\/li>\n<li><strong>Step 6: Determine the <a href=\"https:\/\/www.editage.com\/blog\/p-value-statistics-hypothesis-testing-definition-meaning\/\">p value<\/a> and make a decision.<\/strong> If p-value \u2264 \u03b1 \u2192 reject H\u2080. If p-value &gt; \u03b1 \u2192 insufficient evidence to reject H\u2080, which is not proof that H\u2080 is true. <a href=\"https:\/\/www.geeksforgeeks.org\/data-science\/understanding-hypothesis-testing\/\">\u00a0<\/a><\/li>\n<li><strong>Step 7: Interpret results in plain language.<\/strong> Report the effect size, direction of difference, and p value. State the conclusion in the context of the original research question.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"What_are_Type_I_and_Type_II_Errors\"><\/span>What are Type I and Type II Errors?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A <a href=\"https:\/\/www.editage.com\/blog\/type-i-type-ii-errors-hypothesis-testing\/#What_Is_a_Type_I_Error\">Type I error<\/a> occurs when we reject the null hypothesis although that hypothesis was true. A <a href=\"https:\/\/www.editage.com\/blog\/type-i-type-ii-errors-hypothesis-testing\/#What_Is_a_Type_II_Error\">Type II error<\/a> occurs when we fail to reject the null hypothesis even though it is false.<\/p>\n<ul>\n<li><strong>Type I error (\u03b1):<\/strong> Concluding a new antidepressant works when it actually doesn&#8217;t: leading to unnecessary prescription and costs.<\/li>\n<li><strong>Type II error (\u03b2):<\/strong> Concluding a drug doesn&#8217;t work when it actually does: a missed therapeutic opportunity.<\/li>\n<li><strong>Statistical Power (1 \u2212 \u03b2):<\/strong> The probability of correctly detecting a real effect. Power is typically set at 0.80 in study planning, meaning researchers accept a 20% chance of missing a real effect.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Limitations_of_Null_Hypothesis_Significance_Testing\"><\/span>Limitations of Null Hypothesis Significance Testing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>NHST has attracted intense criticism over the past few decades, especially in light of the replication crisis in psychology and biomedicine. Key limitations include:<\/p>\n<ul>\n<li><strong>Binary thinking:<\/strong> Forcing a rich continuum of evidence into &#8220;significant&#8221; or &#8220;not significant&#8221; loses information and encourages all-or-nothing interpretation.<\/li>\n<li><strong><a href=\"https:\/\/www.editage.com\/insights\/have-you-fallen-prey-to-data-dredging\">P-hacking<\/a> and researcher degrees of freedom:<\/strong> Flexible data collection, analysis choices, and selective reporting inflate the false-positive rate far above the nominal \u03b1.<\/li>\n<li><strong>The file-drawer problem and <a href=\"https:\/\/www.editage.com\/insights\/publication-and-reporting-biases-and-how-they-impact-publication-of-research\">publication bias<\/a>:<\/strong> Studies that fail to reject H\u2080 are less likely to be published, biasing the published literature toward positive findings.<\/li>\n<li><strong>Conflation of statistical and practical significance:<\/strong> A study of 100,000 patients may find that a drug lowers blood pressure by 0.5 mmHg with p &lt; 0.0001: statistically overwhelming, clinically irrelevant.<\/li>\n<li><strong>Data quality dependence:<\/strong> The accuracy of the results depends on the quality of the data. Poor-quality or inaccurate data can lead to incorrect conclusions.<\/li>\n<li><strong>Context limitations:<\/strong> Hypothesis testing doesn&#8217;t always consider the bigger picture, which can oversimplify results and lead to incomplete insights.<\/li>\n<li><strong>Assumption violations:<\/strong> Most standard tests assume <a href=\"https:\/\/www.editage.com\/blog\/normality-test-methods-of-assessing-normality\/\">normally distributed data<\/a>, independent observations, and equal variances. Violations can distort p values.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Best_practices_to_mitigate_these_limitations\"><\/span>Best practices to mitigate these limitations:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Pre-register hypotheses and analysis plans (e.g., on OSF or ClinicalTrials.gov)<\/li>\n<li>Report <a href=\"https:\/\/www.editage.com\/blog\/effect-size\/\">effect sizes<\/a> and <a href=\"https:\/\/www.editage.com\/blog\/what-is-confidence-intervals-and-why-is-it-important\/\">confidence intervals<\/a> alongside p values<\/li>\n<li>Use <a href=\"https:\/\/www.editage.com\/blog\/sample-size-and-statistical-power-definition-formulas-calculations-worked-examples\/\">sufficiently powered studies<\/a> (plan for \u226580% power)<\/li>\n<li>Replicate findings before drawing firm conclusions<\/li>\n<li>Consider Bayesian approaches or equivalence testing where appropriate<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions_FAQs\"><\/span>Frequently Asked Questions (FAQs)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"What_is_statistical_power_and_why_does_it_matter\"><\/span>What is statistical power, and why does it matter?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Statistical power (1 \u2212 \u03b2) is the probability that a test will correctly detect a true effect when one exists. A study with 50% power has only a coin-flip chance of finding a real effect. Low power wastes resources and produces unreliable findings. Power depends on <a href=\"https:\/\/www.editage.com\/blog\/sample-size-and-statistical-power-definition-formulas-calculations-worked-examples\/\">sample size<\/a>, effect size, and \u03b1. Most disciplines target at least 80% power during <a href=\"https:\/\/www.editage.com\/blog\/types-of-study-designs-in-biomedical-research\/\">study design<\/a>, requiring formal power calculations before data collection.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_is_a_confidence_interval_related_to_a_hypothesis_test\"><\/span>How is a confidence interval related to a hypothesis test?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A 95% <a href=\"https:\/\/www.editage.com\/blog\/what-is-confidence-intervals-and-why-is-it-important\/\">confidence interval<\/a> (CI) and a two-tailed test at \u03b1 = 0.05 convey equivalent information: if the CI excludes the null value (e.g., zero for a mean difference), the corresponding p value will be below 0.05. CIs are often preferred because they communicate both the direction and the magnitude of the effect, not just whether it passed a threshold. Reporting both the p value and the CI is considered best practice.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_does_it_mean_to_%E2%80%9Cpre-register%E2%80%9D_a_study\"><\/span>What does it mean to &#8220;pre-register&#8221; a study?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Pre-registration means publicly documenting your hypotheses, data collection plan, and analysis strategy before collecting data, typically through platforms like ClinicalTrials.gov (biomedical) or the Open Science Framework (social sciences). This prevents researchers from unconsciously adjusting their hypotheses or analysis methods after seeing results (HARKing: Hypothesising After Results are Known), which inflates the false-positive rate and undermines reproducibility.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"When_should_I_use_a_non-parametric_test_instead_of_a_t-test_or_ANOVA\"><\/span>When should I use a non-parametric test instead of a t-test or ANOVA?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Parametric tests like t-tests and <a href=\"https:\/\/www.editage.com\/blog\/anova-types-uses-assumptions-a-quick-guide-for-biomedical-researchers\/\">ANOVA<\/a> assume the data are approximately normally distributed. When sample sizes are small and data are strongly skewed, heavily bounded (e.g., Likert scales with small n), or contain extreme outliers, non-parametric alternatives are more appropriate. Common examples include the Mann-Whitney U test (instead of independent t-test), Wilcoxon signed-rank test (instead of paired t-test), and Kruskal-Wallis test (instead of one-way ANOVA). <a href=\"https:\/\/www.editage.com\/insights\/an-introduction-to-non-parametric-tests-for-biomedical-researchers\">Non-parametric tests<\/a> sacrifice some statistical power in exchange for fewer distributional assumptions.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_is_the_difference_between_a_one-sample_and_two-sample_test\"><\/span>What is the difference between a one-sample and two-sample test?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A one-sample test compares a single group&#8217;s mean (or proportion) to a known or hypothesised population value. Example: testing whether the mean birth weight in a hospital differs from the national standard of 3.2 kg. A two-sample test compares the means (or proportions) of two independent groups.<\/p>\n<p>Example: testing whether mean depression scores differ between patients receiving CBT and those receiving pharmacotherapy. When the two sets of measurements come from the same individuals at different times (e.g., pre- and post-intervention), a paired test is used instead.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Can_hypothesis_testing_be_used_with_observational_data_or_only_with_experiments\"><\/span>Can hypothesis testing be used with observational data, or only with experiments?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>NHST applies to both <a href=\"https:\/\/www.editage.com\/blog\/types-of-experimental-research-designs\/\">experimental<\/a> and <a href=\"https:\/\/www.editage.com\/blog\/observational-study\/\">observational<\/a> data, but the conclusions that can be drawn differ. Randomised controlled trials (RCTs) allow causal inference: if the test rejects H\u2080, the intervention is likely the cause. In observational studies (e.g., <a href=\"https:\/\/www.editage.com\/blog\/questionnaire-survey-research\/\">survey data<\/a>, <a href=\"https:\/\/www.editage.com\/blog\/cohort-study\/\">cohort studies<\/a>), NHST can detect associations but cannot establish causation because of potential <a href=\"https:\/\/www.editage.com\/blog\/confounding-variables-identification-definition-types-examples\/\">confounding<\/a>. A statistically significant association between coffee consumption and reduced Parkinson&#8217;s disease risk, for instance, does not by itself prove that coffee is protective because unmeasured lifestyle confounders may explain the association.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"Hypothesis testing is a statistical method used to determine whether there is enough evidence in sample data to draw conclusions about a population. Instead of collecting data from an entire population, you take a sample and test whether the evidence supports or contradicts an assumption about that population. What Is Null Hypothesis Significance Testing (NHST)? [&hellip;]","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"Null Hypothesis Significance Testing (NHST) refers to one particular procedure: you begin by assuming the null hypothesis is true, collect data, compute a test statistic, and then ask how probable your observed result would be under that assumption. The null hypothesis (H\u2080) is a statement of \"no difference,\" \"no association,\" or \"no treatment effect.\" The alternative hypothesis (H\u2090) is a statement of \"difference,\" \"association,\" or \"treatment effect.\" H\u2080 is assumed to be true until proven otherwise. The P value answers the question: \"If the null hypothesis were true, what is the probability of observing the current data or data that is more extreme?\" Note that the P value is NOT the probability that the hypothesis (or any other hypothesis) is right or wrong.","_ayudawp_aiss_summary_provider":"extractive","_ayudawp_aiss_summary_hash":"b2a536ec0878659fe3faca159f9ff0f4b8cc59c8"},"categories":[14],"tags":[23,24],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Hypothesis Testing and NHST: Definition, Steps, Tips, Examples<\/title>\n<meta name=\"description\" content=\"Learn the basics of hypothesis testing, what is the null hypothesis, what is the alternative hypothesis, p value, confidence intervals, effect size.\" \/>\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\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hypothesis Testing and NHST: Definition, Steps, Tips, Examples\" \/>\n<meta property=\"og:description\" content=\"Learn the basics of hypothesis testing, what is the null hypothesis, what is the alternative hypothesis, p value, confidence intervals, effect size.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\" \/>\n<meta property=\"og:site_name\" content=\"Educational Articles For Researchers, Students And Authors - Editage Blog\" \/>\n<meta property=\"article:published_time\" content=\"2023-03-21T10:53:32+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-12T16:10:43+00:00\" \/>\n<meta name=\"author\" content=\"Editor Editor\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Editor Editor\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/\"},\"author\":{\"name\":\"Editor Editor\",\"@id\":\"https:\/\/www.editage.com\/blog\/#\/schema\/person\/194519c669bbbc38e9ed47cc02c5a44f\"},\"headline\":\"Hypothesis Testing and NHST: Definition, Steps, Tips, Examples\",\"datePublished\":\"2023-03-21T10:53:32+00:00\",\"dateModified\":\"2026-09-12T16:10:43+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/\"},\"wordCount\":1607,\"publisher\":{\"@id\":\"https:\/\/www.editage.com\/blog\/#organization\"},\"keywords\":[\"Statistical Analysis Services\",\"Statistical Review Services\"],\"articleSection\":[\"Research Tips\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-nhst\/\",\"url\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\",\"name\":\"Hypothesis Testing and NHST: Definition, Steps, Tips, Examples\",\"isPartOf\":{\"@id\":\"https:\/\/www.editage.com\/blog\/#website\"},\"datePublished\":\"2023-03-21T10:53:32+00:00\",\"dateModified\":\"2026-09-12T16:10:43+00:00\",\"description\":\"Learn the basics of hypothesis testing, what is the null hypothesis, what is the alternative hypothesis, p value, confidence intervals, effect size.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.editage.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Hypothesis Testing and NHST: Definition, Steps, Tips, Examples\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.editage.com\/blog\/#website\",\"url\":\"https:\/\/www.editage.com\/blog\/\",\"name\":\"Educational Articles For Researchers, Students And Authors - Editage Blog\",\"description\":\"Get insightful educational articles from the world of academia for researchers, students and authors. 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