Power analysis is a statistical procedure to determine the sample size of a study that is needed to detect a predefined difference between conditions with a specified level of confidence. Determining your required sample size beforehand (and sticking to it) reduces the chance of a false positive (type 1 error) which increases the replicability of a study. ---------- **More information** [Detailed explanation of the concept of statistical power][1] ---------- **Articles** [When Power Analyses Based on Pilot Data are Biased: Inaccurate Effect Size Estimators and Follow-up Bias (Albers & Lakens, 2018)][2] [Using Anchor-Based Methods to Determine the Smallest Effect Size of Interest (Anvari & Lakens, 2019)][3] [Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods (Carter et al., 2019)][4] [Safeguard Power as a Protection Against Imprecise Power Estimates (Perugini et al., 2014)][5] [A tutorial on Bayes Factor Design Analysis using an informed prior (Stefan et al., 2019)][6] ---------- **Tools** [G*Power][7] [Statistical power analysis in R][8] [Bayesian sample size planning][9] [1]: https://medium.com/data-science-community-srm/statistical-power-and-power-analysis-98cf4e10b064 [2]: https://pdf.sciencedirectassets.com/272387/1-s2.0-S0022103117X0005X/1-s2.0-S002210311630230X/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEP///////////wEaCXVzLWVhc3QtMSJHMEUCIGs7fEJsnaVPo/7SaCzJMCQnGVQNSXOXaMTiRMjvaaRwAiEAkmPc7nulJ456bnnQNKuhQ%2bPYNEa7Y6unJUyS%2bGVu1Y4qsgUIJxAFGgwwNTkwMDM1NDY4NjUiDDEMAqfu8dEu/SyCaiqPBdmuYaBlcWmDBYrESQbXnPW0fxZIA5iDBI9sQo1AQg8h6oiVnxHgWtsW1r/WIYu0VmT2N/5QzAp/EXnvSQ4Y9MQ4akUUkgN0AyJuVHWzRBS6/TIk1dn%2bzaGTU0iCXxuZiW9fmq3lfBoPasHTwA89nJu6rEAFP7PEku3moQMptQYvjJGV0xU25qj/w/UDKxmIMMLwmyxZkTMTRD9Wu7JOCNCF2ueL2GxT5pGJQa%2bAoy8ZePq3aVT0qCUYsU0mGD3Q7attGxrs98B4TGxn/Xim7ePB0mPfxeVW2tvBbk7WYICXc7KUeMgwFviUGa9ha%2bxy9B3dHgJsKLthJ6ijjZqqnbLiJBbu3Iwjl671tuqaWsR2MT/TxIkqtIH4irW87eYSt8WP9YcxHsgGc48rbSu9XbhCS502iFLLGwggRQUCWzS1q8H0eco/5rLwlnkdHzXEX0e2Cc2RpW0ypzH%2bBvZd5jN1N3riSitCVNkpMeXwz9Am/RVUleEaGx%2bSqqsqkuawu89fGVigbPKIKa8nO8J%2bZJokNYEptGV8IAbi4J0wNdUTVVdFBse0p/T7WAxdo7V02ecjnPNU709aS0pa3AXitpI4GotQpTnRhYXpvKihJS3I9lAsgHdzjtAGahzJ1qlaPLBXvmK71%2bss3KiVb2w4%2bG%2bYYSUEh1ratKZGoKLlslahmILjH6QZS/7H0IGhJID7sjSB9KGPS0mtQXy%2bdTFl5jQYWNNW094pUZ4kxFNBcRMqCofhJWTmlg4YjlWVVRPKY9hSdh6SeQx/GWaPLwQwDSwlbEHsbMs6/glT12kC8CZPVEDIWRV%2b1VJx8mAQ3ZL59/qWAhcXhkNtZPmT513s7t19dneh9zcJZ2jRl4JXHusw053YqQY6sQHgGk2vzFeYUakSSnNekBjxmJ%2bnPLn3jF3nmjoN8Hl8QaNOstLvz7Ta4Q3rATGXCWLPlQgCDW1SJ1tj9w3FgoVVw750YZNck2VqHsoH6bZ8MklDuc0/Ro6pmxr3iQQzPm7gkQu1GYdI%2byjF9jBn24ODgD7eMAD5mcUHERlfujzdpkTVB5ppLnsrEsYWvxRWNyr7eB1EhDUvl9f9Fbw9Gtdi%2buQup33swkF/yNYTIbzOUCA=&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20231023T071330Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY6J3JQHWP/20231023/us-east-1/s3/aws4_request&X-Amz-Signature=6668947d45442c97bc3e4abfb1bbc15600f0f71f1b40f07eec906233f2e5c6e7&hash=c1dadba7bf9d65f8ceff766a8e261599bbc5cfb229d477cb1a5aaec40d3ef7c3&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S002210311630230X&tid=spdf-0bca7e82-0cc2-40a5-9bf5-a6d98695a647&sid=d97174c73a1082418d0a97d-b612148d4546gxrqb&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1403595e0e5654025950&rr=81a82d861e6e66c0&cc=nl [3]: https://osf.io/preprints/psyarxiv/syp5a/ [4]: https://journals.sagepub.com/doi/10.1177/2515245919847196 [5]: https://journals.sagepub.com/doi/10.1177/1745691614528519 [6]: https://link.springer.com/article/10.3758/s13428-018-01189-8 [7]: https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html [8]: https://www.programmingr.com/examples/neat-tricks/sample-r-function/how-to-seize-pwr-statistical-power-analysis-in-r/ [9]: https://github.com/nicebread/BFDA