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#poweranalysis

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Dr Mircea Zloteanu ☀️ 🌊🌴<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statstab</span></a> #371 Safeguard Power as a Protection Against Imprecise Power Estimates</p><p>Thoughts: tl;dr - when replicating a study, use the lower end of the CI of the original study as your effect in a power analysis.</p><p><a href="https://mastodon.social/tags/poweranalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>poweranalysis</span></a> <a href="https://mastodon.social/tags/samplesize" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>samplesize</span></a> <a href="https://mastodon.social/tags/replication" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>replication</span></a></p><p><a href="https://journals.sagepub.com/doi/10.1177/1745691614528519" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/10.11</span><span class="invisible">77/1745691614528519</span></a></p>

Hello #statstodon! A reviewer asks me to perform a post-hoc #PowerAnalysis. I know this is generally not advised because if you replace the a priori effect size by the effect size measured in the experiment, this will introduce an erroneous relationship between the significance level of the test and the measured power.
… but does that mean that there is no proper way of measuring power retrospectively? For example, if you refrain from using the measured effect size and instead simulate a range of “a priori” effect size unrelated to the results of the test, then the dependency of the power to the significance level should not happen?
#stats #statschat @lakens