{"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":"2024-08-09T14:10:03","modified_gmt":"2024-08-09T14:10:03","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":"An introduction to sample size, effect size, and statistical power for biomedical researchers"},"content":{"rendered":"

Ever wondered how many participants you need to ensure your findings are solid? How likely is it that the relationship<\/a> you\u2019ve detected actually exists and isn\u2019t due to chance? Excited to learn how to go beyond p values<\/a> in hypothesis testing<\/a>? Look no further! The infographic below demystifies sample size, statistical power, and effect size<\/a> for biomedical researchers, breaking down these concepts into bite-sized nuggets so that you can design studies<\/a> that stand up to scrutiny and make a real impact.<\/p>\n

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