{"id":5585,"date":"2023-10-03T06:32:18","date_gmt":"2023-10-03T06:32:18","guid":{"rendered":"https:\/\/www.editage.com\/insights\/comic_strip\/an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers\/"},"modified":"2026-04-22T08:53:10","modified_gmt":"2026-04-22T03:23:10","slug":"an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers","status":"publish","type":"comic_strip","link":"https:\/\/www.editage.com\/insights\/an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers","title":{"rendered":"Sample size, effect size, and statistical power: A guide"},"content":{"rendered":"<p>Ever wondered how many participants you need to ensure your findings are solid? How likely is it that the <a title=\"https:\/\/www.editage.com\/insights\/best-practices-in-reporting-correlations-associations-and-regressions-in-biomedical-research?refer=insights-search-posts\" href=\"https:\/\/www.editage.com\/insights\/best-practices-in-reporting-correlations-associations-and-regressions-in-biomedical-research?refer=insights-search-posts\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Link relationship\">relationship<\/a> you\u2019ve detected actually exists and isn\u2019t due to chance? Excited to learn how to go beyond <a title=\"https:\/\/www.editage.com\/insights\/correct-way-report-p-values?refer=insights-search-posts\" href=\"https:\/\/www.editage.com\/insights\/correct-way-report-p-values?refer=insights-search-posts\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Link p values\">p values<\/a> in <a title=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\" href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Link hypothesis testing\">hypothesis testing<\/a>? Look no further! The infographic below demystifies sample size, statistical power, and <a title=\"https:\/\/www.editage.com\/insights\/linking-statistical-significance-to-clinical-importance-of-trial-data-a-paradigm-shift?refer=insights-search-posts\" href=\"https:\/\/www.editage.com\/insights\/linking-statistical-significance-to-clinical-importance-of-trial-data-a-paradigm-shift?refer=insights-search-posts\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Link effect size\">effect size<\/a> for biomedical researchers, breaking down these concepts into bite-sized nuggets so that you can <a title=\"https:\/\/www.editage.com\/insights\/linking-statistical-significance-to-clinical-importance-of-trial-data-a-paradigm-shift?refer=insights-search-posts\" href=\"https:\/\/www.editage.com\/insights\/linking-statistical-significance-to-clinical-importance-of-trial-data-a-paradigm-shift?refer=insights-search-posts\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Link design studies\">design studies<\/a> that stand up to scrutiny and make a real impact.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"media-element file-default alignnone\" title=\"Default Title Text\" src=\"https:\/\/insights.cactusglobal.com\/sites\/default\/files\/CCGMC_40_Inofgraphic_02_Oct_2023_1060x800.jpg\" alt=\"An infographic explaining Sample Size, Effect Size, and Statistical Power for Biomedical Researchers Sample Size \u2022 Sample size refers to the number of individuals or data points you include in your research study. \u2022 It directly affects the reliability of your findings. Statistical Power \u2022 Statistical power is the likelihood of detecting a true relationship, effect, or difference if it exists in your data. \u2022 You need adequate power if you want to avoid false-positive and false-negative errors. Effect Size \u2022 Effect size tells you whether the differences or relationships that you detect are meaningful in real life. \u2022 Larger sample sizes are needed to detect smaller effects accurately. \u2022 Biomedical researchers often focus on numerically small differences (e.g., normal vs prediabetes HbA1c levels), so sample size calculations are crucial. Benefits of an Adequate Sample and Statistical Power Representativeness: \u2022 A larger sample size can better represent the diversity within the population you're studying. \u2022 This reduces the chance of drawing incorrect conclusions based on a biased sample. Precision and Confidence: \u2022 A larger sample size leads to more precise estimates and narrower confidence intervals. \u2022 This means you can have more confidence in the accuracy of your results. Efficient Use of Resources: \u2022 Adequate power means that your findings are likely to be high-quality. \u2022 You don\u2019t waste resources on an underpowered study that yields unreliable results. \u2022 You don\u2019t waste resources on an unnecessarily large sample size, inconveniencing participants. \" width=\"1060\" height=\"800\" \/><\/p>\n","protected":false},"author":15,"featured_media":46445,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false},"categories":[2420,2415,2403],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-5585","comic_strip","type-comic_strip","status-publish","has-post-thumbnail","hentry","category-data-analysis","category-data-storage-management","category-publication-support-services"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Sample size, effect size, and statistical 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