<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Statistical Power on Adventures in Why</title><link>https://www.adventuresinwhy.com/tag/statistical-power/</link><description>Recent content in Statistical Power on Adventures in Why</description><generator>Hugo</generator><language>en-us</language><managingEditor>bob@ConvexAnalytics.com (Bob Wilson)</managingEditor><webMaster>bob@ConvexAnalytics.com (Bob Wilson)</webMaster><copyright>&amp;copy; Bob Wilson {year}</copyright><lastBuildDate>Thu, 12 Aug 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://www.adventuresinwhy.com/tag/statistical-power/index.xml" rel="self" type="application/rss+xml"/><item><title>Robust Power Assessment</title><link>https://www.adventuresinwhy.com/post/robust_power_assessment/</link><pubDate>Thu, 12 Aug 2021 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/robust_power_assessment/</guid><description>&lt;p&gt;An important part of planning any statistical experiment is power analysis. In
this post I will focus on power analysis for linear regression models, but I am
hopeful much of this can be applied to Generalized Linear Models and hence to
the sorts of A/B tests I normally run.&lt;/p&gt;</description></item><item><title>Optimal Experiment Design</title><link>https://www.adventuresinwhy.com/post/optimal-experiment-design/</link><pubDate>Sat, 01 Jun 2019 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/optimal-experiment-design/</guid><description>We can plan sample sizes to control the width of confidence intervals.</description></item><item><title>Statistical Power</title><link>https://www.adventuresinwhy.com/post/ab-testing-power/</link><pubDate>Tue, 19 Jun 2018 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/ab-testing-power/</guid><description>Power considerations drive the sample sizes needed for a successful experiment.</description></item></channel></rss>