<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Confidence Intervals on Adventures in Why</title><link>https://www.adventuresinwhy.com/tag/confidence-intervals/</link><description>Recent content in Confidence Intervals 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, 03 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.adventuresinwhy.com/tag/confidence-intervals/index.xml" rel="self" type="application/rss+xml"/><item><title>Randomization Inference for Matched Pairs with Binary Outcomes</title><link>https://www.adventuresinwhy.com/publication/matched-pairs-binary-outcomes/</link><pubDate>Thu, 03 Sep 2026 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/publication/matched-pairs-binary-outcomes/</guid><description/></item><item><title>Scheffe's Method for Multiple Comparisons</title><link>https://www.adventuresinwhy.com/post/scheffes_method/</link><pubDate>Sun, 01 Aug 2021 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/scheffes_method/</guid><description>I&amp;rsquo;ve written previously about using the Bonferroni correction for the multiple comparisons problem. While it is without a doubt the simplest way to correct for multiple comparisons, it is not the only way. In this post, I discuss Scheffé&amp;rsquo;s method for constructing simultaneous confidence intervals on arbitrarily many functions of the model parameters.</description></item><item><title>Contingency Tables Part IV: The Score Test</title><link>https://www.adventuresinwhy.com/post/score_test/</link><pubDate>Tue, 24 Nov 2020 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/score_test/</guid><description>The score test can be used to calculate p-values and confidence intervals for A/B tests. The score test considers the slope of the likelihood function at the parameter value associated with the null hypothesis.</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>Confidence Intervals</title><link>https://www.adventuresinwhy.com/post/confidence-intervals/</link><pubDate>Mon, 12 Nov 2018 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/confidence-intervals/</guid><description>Statistical analysis is not complete without an estimate of residual uncertainty.</description></item></channel></rss>