{"id":1786,"date":"2026-05-17T11:06:09","date_gmt":"2026-05-17T05:36:09","guid":{"rendered":"https:\/\/www.editage.com\/insights\/optimizing-research-quality-importance-of-statistical-power-and-how-to-calculate-it-in-biomedical-sciences\/"},"modified":"2026-06-11T14:18:04","modified_gmt":"2026-06-11T08:48:04","slug":"importance-of-statistical-power-in-research-design","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/importance-of-statistical-power-in-research-design","title":{"rendered":"Statistical Power: What It Is, Why It Matters, and How to Calculate It"},"content":{"rendered":"<p><strong>Contents<\/strong><\/p>\n<ul>\n<li><a href=\"#_Toc231233470\">What Is Statistical Power?<\/a><\/li>\n<li><a href=\"#_Toc231233471\">Why Does Statistical Power Matter?<\/a><\/li>\n<li><a href=\"#_Toc231233472\">Statistical Power and Hypothesis Testing: Type I and Type II Errors<\/a><\/li>\n<li><a href=\"#_Toc231233473\">The Four Components of a Power Analysis<\/a><\/li>\n<li><a href=\"#_Toc231233474\">Additional Factors That Affect Power<\/a><\/li>\n<li><a href=\"#_Toc231233475\">When Should You Calculate Statistical Power?<\/a><\/li>\n<li><a href=\"#_Toc231233476\">How to Calculate Statistical Power<\/a><\/li>\n<li><a href=\"#_Toc231233477\">How to Increase Statistical Power<\/a><\/li>\n<li><a href=\"#_Toc231233478\">Statistical Power Across Research Disciplines<\/a><\/li>\n<li><a href=\"#_Toc231233479\">Common Misconceptions About Statistical Power<\/a><\/li>\n<li><a href=\"#_Toc231233480\">Frequently Asked Questions<\/a><\/li>\n<li><a href=\"#_Toc231233481\">Key Takeaways<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>One of the most frequently overlooked determinants of research quality is <strong>statistical power<\/strong>. Underpowered studies waste resources, produce unreliable results, and raise serious ethical concerns especially in clinical settings. Yet many manuscripts still reach peer review without a power calculation.<\/p>\n<p>Understanding statistical power is therefore not just a technical formality\u2014it is central to designing studies that can actually answer the research questions they set out to address. This guide explains what power is, how it relates to <a href=\"https:\/\/www.editage.com\/insights\/everything-you-need-to-know-about-framing-a-research-hypothesis\">hypothesis testing<\/a>, how to calculate it, and how to increase it.<\/p>\n<h2><a name=\"_Toc231233470\"><\/a>What Is Statistical Power?<\/h2>\n<p><strong>Statistical power<\/strong> is the probability that a statistical test will correctly detect a true effect when one actually exists. In other words, it is the likelihood of rejecting a false <a href=\"https:\/\/www.editage.com\/insights\/the-null-hypothesis-what-researchers-often-get-wrong\">null hypothesis<\/a>.<\/p>\n<p>Power is expressed as a value between 0 and 1 (or as a percentage). A study with 80% power has an 80% chance of detecting a real effect if it exists and a 20% chance of missing it entirely.<\/p>\n<p><strong>Power\u00a0 =\u00a0 1 \u2212 \u03b2\u00a0 =\u00a0 P(reject H\u2080 | H\u2080 is false)<\/strong><\/p>\n<p>Where <strong>\u03b2<\/strong> is the probability of a Type II error (false negative).<\/p>\n<h2><a name=\"_Toc231233471\"><\/a>Why Does Statistical Power Matter?<\/h2>\n<p>High statistical power is necessary to draw accurate conclusions about a population from sample data. Reporting guidelines like CONSORT (Consolidated Standards of Reporting Trials) require authors to justify sample size, and the American Psychological Association strongly recommends reporting a power analysis in the methods section of psychology papers.<\/p>\n<h3>Consequences of Low Power<\/h3>\n<p>An underpowered study carries several serious risks:<\/p>\n<ul>\n<li><strong>False negatives (Type II errors): <\/strong>A real effect exists but the study fails to detect it.<\/li>\n<li><strong>Inflated effect size estimates: <\/strong>In low-powered fields (e.g., neuroscience), only large or chance-inflated effects reach significance, systematically overstating true effects.<\/li>\n<li><strong>Wasted resources: <\/strong>Time, funding, and participant burden are expended on studies that cannot yield reliable conclusions.<\/li>\n<li><strong>Ethical issues: <\/strong>Exposing participants\u2014especially patients in clinical trials\u2014to interventions when the study cannot detect a meaningful effect is ethically problematic.<\/li>\n<\/ul>\n<h3>Consequences of Excessively High Power<\/h3>\n<p>More power is not always better. An over-powered study can detect effects so small that they have no clinical or practical significance, potentially leading to misleading conclusions about real-world relevance.<\/p>\n<h3>Why Journals Require Power Calculations<\/h3>\n<p>Journals such as the British Journal of Surgery, JAMA Neurology, and Molecular Genetics and Metabolism require power calculations to be clearly stated in the manuscript. The <a href=\"https:\/\/www.editage.com\/insights\/how-to-write-the-methods-section-of-a-research-paper\">methods section<\/a> must justify the chosen sample size through a transparent a priori power analysis. It is also valuable to include a power calculation in your <a href=\"https:\/\/www.editage.com\/insights\/grant-proposal-writing-part1\">grant proposal<\/a> so that funding reviewers can assess the robustness of the planned study.<\/p>\n<h2><a name=\"_Toc231233472\"><\/a>Statistical Power and Hypothesis Testing: Type I and Type II Errors<\/h2>\n<p>In <a href=\"https:\/\/www.editage.com\/insights\/everything-you-need-to-know-about-framing-a-research-hypothesis\">hypothesis testing<\/a>, you start with a null hypothesis (H\u2080) of no effect and an alternative hypothesis (H\u2081) of a true effect. There are two kinds of errors that can occur:<\/p>\n<table width=\"624\">\n<thead>\n<tr>\n<td width=\"120\"><strong>Error Type<\/strong><\/td>\n<td width=\"160\"><strong>Description<\/strong><\/td>\n<td width=\"172\"><strong>Also Called<\/strong><\/td>\n<td width=\"172\"><strong>Linked To<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td width=\"0\">Type I (\u03b1)<\/td>\n<td colspan=\"3\" width=\"504\"><\/td>\n<\/tr>\n<tr>\n<td width=\"0\">Type II (\u03b2)<\/td>\n<td colspan=\"3\" width=\"504\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>Power = 1 \u2212 \u03b2<\/strong>, so increasing power directly reduces the risk of a Type II error. However, lowering the significance threshold (reducing \u03b1) to guard against Type I errors will reduce power \u2014 the two must be balanced.<\/p>\n<h2><a name=\"_Toc231233473\"><\/a>The Four Components of a Power Analysis<\/h2>\n<p>A power analysis involves four interrelated parameters. If you know any three, you can calculate the fourth. In practice, alpha is usually fixed and <a href=\"https:\/\/www.editage.com\/blog\/effect-size\/\" target=\"_blank\" rel=\"noopener\">effect size<\/a> is estimated from the literature, making sample size the key variable to determine.<\/p>\n<table width=\"624\">\n<thead>\n<tr>\n<td width=\"133\"><strong>Parameter<\/strong><\/td>\n<td width=\"200\"><strong>Definition<\/strong><\/td>\n<td width=\"133\"><strong>Typical Value<\/strong><\/td>\n<td width=\"157\"><strong>Controls<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td width=\"0\">Sample size (N)<\/td>\n<td colspan=\"3\" width=\"491\"><\/td>\n<\/tr>\n<tr>\n<td width=\"0\">Effect size<\/td>\n<td colspan=\"3\" width=\"491\"><\/td>\n<\/tr>\n<tr>\n<td width=\"0\">Significance level (\u03b1)<\/td>\n<td colspan=\"3\" width=\"491\"><\/td>\n<\/tr>\n<tr>\n<td width=\"0\">Power (1 \u2212 \u03b2)<\/td>\n<td colspan=\"3\" width=\"491\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h3>1. Sample Size<\/h3>\n<p>Sample size is positively related to power. Larger samples provide more accurate estimates of population parameters, reducing the standard error and making it easier to detect effects. For a detailed introduction, see <a href=\"https:\/\/www.editage.com\/insights\/an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers\">our guide to sample size, effect size, and statistical power<\/a>.<\/p>\n<p>Note that the research design also matters:<\/p>\n<ul>\n<li><strong>Within-subjects designs<\/strong> (each participant appears in all conditions) are more powerful because individual differences cancel out, requiring a smaller N.<\/li>\n<li><strong>Between-subjects designs<\/strong> (different participants per condition) require larger samples because individual variation can mask the treatment effect.<\/li>\n<\/ul>\n<h3>2. Effect Size<\/h3>\n<p>Effect size measures the magnitude of a difference or relationship between variables \u2014 it reflects practical, not just statistical, significance. Larger effect sizes are easier to detect. You typically estimate the expected effect size by reviewing the <a href=\"https:\/\/www.editage.com\/insights\/from-library-shelves-to-ai-the-transformation-of-literature-search\">literature<\/a> or from a pilot study.<\/p>\n<p>Common effect size metrics include:<\/p>\n<ul>\n<li><strong>Cohen&#8217;s d<\/strong> for comparing two group means (small = 0.2, medium = 0.5, large = 0.8)<\/li>\n<li><strong>r<\/strong> for correlations (small = 0.1, medium = 0.3, large = 0.5)<\/li>\n<li><strong>\u03b7\u00b2 (eta-squared)<\/strong> for ANOVA; see our guide to <a href=\"https:\/\/www.editage.com\/insights\/anova-testing-in-statistics\">ANOVA testing<\/a><\/li>\n<\/ul>\n<p>If low-powered studies dominate a research field, the observed effect sizes will consistently <strong>overestimate<\/strong> true effects, because only chance-inflated large effects survive the significance threshold.<\/p>\n<h3>3. Significance Level (Alpha)<\/h3>\n<p>The significance level (\u03b1) is the maximum probability of committing a Type I error. It is usually set at 0.05, meaning results must have less than a 5% probability of occurring under the null hypothesis to be considered significant. See our guide on <a href=\"https:\/\/www.editage.com\/insights\/correct-way-report-p-values\">how to correctly report p-values<\/a>.<\/p>\n<p>Increasing alpha (e.g., from 0.05 to 0.10) increases power but also increases the false positive rate. Decreasing alpha makes the test more conservative and reduces power.<\/p>\n<h3>4. Power (1 \u2212 \u03b2)<\/h3>\n<p>Power is conventionally set at <strong>80% (0.80)<\/strong> as a minimum. This means that if a true effect exists, the study will detect it 80% of the time. Some fields, particularly clinical trials and high-stakes research, aim for 90% power or higher.<\/p>\n<h2><a name=\"_Toc231233474\"><\/a>Additional Factors That Affect Power<\/h2>\n<h3>Population Variability<\/h3>\n<p>High variability within the population reduces power by making it harder to distinguish a true signal from background noise. Using a more homogeneous population (defined by specific demographic or clinical characteristics) can reduce spread and improve power. This is also related to <a href=\"https:\/\/www.editage.com\/insights\/7-tips-to-avoid-biases-in-biomedical-data-collection\">issues of bias and generalizability<\/a> in your sample.<\/p>\n<h3>Measurement Error<\/h3>\n<p>The higher the measurement error, the lower the statistical power. Measurement error can be:<\/p>\n<ul>\n<li><strong>Random:<\/strong> Unpredictable fluctuations (e.g., mood affecting survey responses)<\/li>\n<li><strong>Systematic:<\/strong> Consistent bias from a source (e.g., miscalibrated instrument, leading survey questions)<\/li>\n<\/ul>\n<p>Reducing measurement error improves reliability and power. Strategies include using validated instruments, standardizing data collection procedures, and applying <a href=\"https:\/\/www.editage.com\/insights\/the-crucial-role-of-blinding-to-avoid-bias-in-research-and-publication\">blinding<\/a> to prevent observer bias.<\/p>\n<h3>Test Type<\/h3>\n<p>Some statistical tests are inherently more powerful than others under specific conditions. For example, a one-tailed test is more powerful than a two-tailed test when the direction of the effect can be predicted in advance. Choosing the right <a href=\"https:\/\/www.editage.com\/insights\/3-simple-steps-to-help-you-pick-the-right-statistical-test\">statistical test<\/a> for your data and <a href=\"https:\/\/www.editage.com\/insights\/qualitative-quantitative-or-mixed-methods-a-quick-guide-to-choose-the-right-design-for-your-research\">study design<\/a> is therefore part of optimizing power.<\/p>\n<h2><a name=\"_Toc231233475\"><\/a>When Should You Calculate Statistical Power?<\/h2>\n<h3>A Priori (Before Data Collection)<\/h3>\n<p><strong>This is the most important and most common type of power analysis.<\/strong> Conducted at the design stage, it tells you the minimum sample size needed to detect your expected effect at a chosen significance level and power. Performing an a priori analysis before starting data collection is essential because it is very difficult to correct for insufficient power after the fact.<\/p>\n<h3>Interim Power Analysis<\/h3>\n<p>For long-term or adaptive studies, interim power analyses allow you to adjust sample sizes as the study progresses, preventing both premature termination (too few participants) and unnecessary prolongation (too many).<\/p>\n<h3>Post Hoc (A Posteriori) Power Analysis<\/h3>\n<p>Conducted after data collection to understand why a result was non-significant. While it can help interpret <a href=\"https:\/\/www.editage.com\/insights\/how-can-i-publish-negative-results\">negative results<\/a>, post hoc power analysis is controversial: observed power calculated from a non-significant result is often misleading and should be interpreted cautiously.<\/p>\n<h2><a name=\"_Toc231233476\"><\/a>How to Calculate Statistical Power<\/h2>\n<p>A power analysis requires inputs for three of the four parameters (sample size, effect size, alpha, power) to calculate the fourth. The general workflow is:<\/p>\n<ul>\n<li><strong>Step 1:<\/strong> Choose your significance level (typically \u03b1 = 0.05)<\/li>\n<li><strong>Step 2:<\/strong> Estimate the expected effect size from published literature or a pilot study<\/li>\n<li><strong>Step 3:<\/strong> Set your desired power level (typically 0.80)<\/li>\n<li><strong>Step 4:<\/strong> Use a power analysis tool to calculate the required sample size<\/li>\n<\/ul>\n<h3>Tools for Power Analysis<\/h3>\n<table>\n<thead>\n<tr>\n<td><strong>Tool<\/strong><\/td>\n<td><strong>Best For<\/strong><\/td>\n<td><strong>Access<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>G*Power<\/td>\n<td>Wide range of tests; free desktop software for t-tests, ANOVA, regression, and more<\/td>\n<td>Free download<\/td>\n<\/tr>\n<tr>\n<td>R (pwr package)<\/td>\n<td>Flexible, scriptable; integrates with analysis pipeline<\/td>\n<td>Free (R language)<\/td>\n<\/tr>\n<tr>\n<td>Python (statsmodels)<\/td>\n<td>Power analysis in data science workflows<\/td>\n<td>Free (Python library)<\/td>\n<\/tr>\n<tr>\n<td>PASS (NCSS)<\/td>\n<td>Clinical trials; extensive test coverage with detailed reporting<\/td>\n<td>Commercial<\/td>\n<\/tr>\n<tr>\n<td>WebPower (online)<\/td>\n<td>Quick browser-based calculations; no installation needed<\/td>\n<td>Free online<\/td>\n<\/tr>\n<tr>\n<td>SAS PROC POWER<\/td>\n<td>Widely used in pharmaceutical and regulatory settings<\/td>\n<td>Commercial (SAS license)<\/td>\n<\/tr>\n<tr>\n<td>Stata (power command)<\/td>\n<td>Popular in epidemiology and social sciences<\/td>\n<td>Commercial (Stata license)<\/td>\n<\/tr>\n<tr>\n<td>PowerUp!<\/td>\n<td>Hierarchical\/clustered study designs (e.g., school-based trials)<\/td>\n<td>Free download<\/td>\n<\/tr>\n<tr>\n<td>Optimal Design Plus<\/td>\n<td>Multilevel and longitudinal study designs<\/td>\n<td>Free download<\/td>\n<\/tr>\n<tr>\n<td>PS Power &amp; Sample Size<\/td>\n<td>Clinical and epidemiological studies; produces publication-ready output<\/td>\n<td>Free download<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><\/h3>\n<h3>Worked Example<\/h3>\n<p>A researcher wants to detect a medium effect size (Cohen&#8217;s d = 0.5) with 80% power at \u03b1 = 0.05 using a two-tailed independent-samples t-test. Entering these values into G*Power returns a required sample size of approximately N = 51 per group (102 total). If the researcher increases desired power to 90%, the required N rises to approximately 68 per group.<\/p>\n<p>If you need expert guidance, Editage&#8217;s <a href=\"https:\/\/www.editage.com\/services\/publishing-services-packs\/statistical-analysis\">Statistical Analysis &amp; Review Service<\/a> connects you with qualified biostatisticians who can help you plan and execute your power calculations.<\/p>\n<h2><a name=\"_Toc231233477\"><\/a>How to Increase Statistical Power<\/h2>\n<p>If a power analysis reveals that your planned study is underpowered, you have several options:<\/p>\n<table>\n<thead>\n<tr>\n<td><strong>Strategy<\/strong><\/td>\n<td><strong>How It Helps<\/strong><\/td>\n<td><strong>Trade-off<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Increase sample size<\/td>\n<td>Directly reduces standard error, increasing precision<\/td>\n<td>Higher cost and time<\/td>\n<\/tr>\n<tr>\n<td>Increase effect size<\/td>\n<td>Manipulate IV more strongly; tighten inclusion criteria<\/td>\n<td>May reduce generalizability<\/td>\n<\/tr>\n<tr>\n<td>Increase alpha (e.g., 0.10)<\/td>\n<td>Lowers the detection threshold<\/td>\n<td>More Type I errors<\/td>\n<\/tr>\n<tr>\n<td>Reduce measurement error<\/td>\n<td>Validated instruments, blinding, standardized protocols<\/td>\n<td>Requires more careful design<\/td>\n<\/tr>\n<tr>\n<td>Use a within-subjects design<\/td>\n<td>Individual differences controlled within participants<\/td>\n<td>Order\/carryover effects risk<\/td>\n<\/tr>\n<tr>\n<td>Use a one-tailed test<\/td>\n<td>Higher power when direction of effect is known a priori<\/td>\n<td>Cannot detect opposite effect<\/td>\n<\/tr>\n<tr>\n<td>Control confounding variables<\/td>\n<td>Reduces residual variability<\/td>\n<td>More complex analysis<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Choosing the right <a href=\"https:\/\/www.editage.com\/insights\/sampling-methods-and-techniques-in-research-a-comprehensive-guide\">sampling method<\/a> and controlling for <a href=\"https:\/\/www.editage.com\/insights\/taming-outliers-in-biomedical-research-a-handy-guide\">outliers<\/a> and <a href=\"https:\/\/www.editage.com\/insights\/statistical-solutions-to-overcome-missing-data-in-clinical-trials-and-observational-studies\">missing data<\/a> also contribute to maintaining power in practice.<\/p>\n<h2><a name=\"_Toc231233478\"><\/a>Statistical Power Across Research Disciplines<\/h2>\n<p>Statistical power is not unique to biomedical research. Reporting standards vary by field:<\/p>\n<ul>\n<li><strong>Biomedical sciences: <\/strong>CONSORT and SPIRIT guidelines require sample size justification based on power calculations in randomized trials.<\/li>\n<li><strong>Psychology: <\/strong>The APA&#8217;s Reporting Standards for Research in Psychology strongly recommend power analyses in the methods section.<\/li>\n<li><strong>Clinical trials: <\/strong>Regulatory agencies (e.g., FDA, EMA) require power calculations in protocols. Interim analyses are often mandated.<\/li>\n<li><strong>Social sciences: <\/strong>Power analysis is increasingly expected, particularly in pre-registration of study protocols.<\/li>\n<\/ul>\n<h2><a name=\"_Toc231233479\"><\/a>Common Misconceptions About Statistical Power<\/h2>\n<table>\n<thead>\n<tr>\n<td><strong>Misconception<\/strong><\/td>\n<td><strong>Reality<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>&#8220;A rigorous methodology compensates for low power&#8221;<\/td>\n<td>Power is independent of methodology. A randomized controlled trial can still be underpowered.<\/td>\n<\/tr>\n<tr>\n<td>&#8220;A non-significant result means no effect exists&#8221;<\/td>\n<td>It may simply mean the study lacked sufficient power to detect it.<\/td>\n<\/tr>\n<tr>\n<td>&#8220;More data is always better&#8221;<\/td>\n<td>Beyond a certain N, each additional observation adds marginal benefit; costs increase without proportional gain.<\/td>\n<\/tr>\n<tr>\n<td>&#8220;Post hoc power analysis validates my result&#8221;<\/td>\n<td>Observed power from a non-significant test is mathematically redundant with the p-value and should not be used.<\/td>\n<\/tr>\n<tr>\n<td>&#8220;80% power is always sufficient&#8221;<\/td>\n<td>High-stakes decisions (e.g., drug approval, clinical guidelines) may require 90\u201395% power.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2><a name=\"_Toc231233480\"><\/a>Frequently Asked Questions<\/h2>\n<h3>What is a good statistical power value?<\/h3>\n<p>The convention in most fields is a minimum of 80% (0.80). This means a 20% chance of a Type II error is considered acceptable. In clinical trials or high-stakes contexts, 90% is often recommended.<\/p>\n<h3>What is the difference between statistical power and statistical significance?<\/h3>\n<p>Statistical significance (governed by the p-value and alpha level) controls the risk of a false positive (Type I error). Statistical power controls the risk of a false negative (Type II error). The two are related but distinct: a study can be statistically significant yet underpowered, or highly powered yet non-significant.<\/p>\n<h3>Can I calculate power after the study is complete?<\/h3>\n<p>Yes, but post hoc power analysis has serious limitations. Observed power is directly determined by the p-value and adds no new information. It is more informative to report confidence intervals, which directly express the precision of your estimates.<\/p>\n<h3>How does effect size affect the required sample size?<\/h3>\n<p>Inversely. A larger expected effect size requires fewer participants to achieve a given power level. A small effect (d = 0.2) requires a much larger N than a large effect (d = 0.8) at the same alpha and power.<\/p>\n<h3>What if my study has multiple outcomes?<\/h3>\n<p>Multiple testing increases the risk of Type I errors and affects power. Corrections like Bonferroni adjustment or False Discovery Rate control should be considered. Consult our guide on <a href=\"https:\/\/www.editage.com\/insights\/3-simple-steps-to-help-you-pick-the-right-statistical-test\">choosing the right statistical test<\/a> for more guidance.<\/p>\n<h2><a name=\"_Toc231233481\"><\/a>Key Takeaways<\/h2>\n<ul>\n<li>Power is the probability of detecting a true effect; it should be at least 80%.<\/li>\n<li>Power depends on four interrelated factors: sample size, effect size, alpha, and power itself.<\/li>\n<li>Always perform an a priori power analysis before data collection.<\/li>\n<li>Low power wastes resources, produces unreliable estimates, and raises ethical concerns.<\/li>\n<li>Multiple tools (G*Power, R, Python) can help you run power calculations efficiently.<\/li>\n<\/ul>\n<p>If you need support with power calculations, sample size planning, or other statistical analyses, consider Editage&#8217;s <a href=\"https:\/\/www.editage.com\/services\/publishing-services-packs\/statistical-analysis\">Statistical Analysis &amp; Review Service<\/a>.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Contents What Is Statistical Power? Why Does Statistical Power Matter? Statistical Power and Hypothesis Testing: Type I and Type II Errors The Four Components of a Power Analysis Additional Factors That Affect Power When Should You Calculate Statistical Power? How to Calculate Statistical Power How to Increase Statistical Power Statistical Power Across Research Disciplines Common [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"","_ayudawp_aiss_summary_provider":"","_ayudawp_aiss_summary_hash":"","footnotes":""},"categories":[2420],"tags":[1319],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-1786","post","type-post","status-publish","format-standard","hentry","category-data-analysis","tag-statistical-analysis"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Optimizing research quality: Importance of statistical power and how to calculate it in biomedical sciences | Editage Insights<\/title>\n<meta name=\"description\" content=\"In statistics, \u201cpower\u201d refers to the ability of your study to identify effects of substantial interest. 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