<?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 Chen Kai Blog</title><link>https://www.chenk.top/en/tags/confidence-intervals/</link><description>Recent content in Confidence Intervals on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 28 Aug 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/confidence-intervals/index.xml" rel="self" type="application/rss+xml"/><item><title>Probability and Statistics (7): Hypothesis Testing — p-Values, Confidence Intervals, and All Their Pitfalls</title><link>https://www.chenk.top/en/probability-statistics/07-hypothesis-testing/</link><pubDate>Wed, 28 Aug 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/probability-statistics/07-hypothesis-testing/</guid><description>&lt;p>You&amp;rsquo;ve estimated a parameter. You&amp;rsquo;ve quantified the bias-variance tradeoff. Now comes the question that drives most applied statistics: &amp;ldquo;Is this effect real, or just noise?&amp;rdquo;&lt;/p>
&lt;p>Hypothesis testing is the formal framework for answering this question. It&amp;rsquo;s also the most widely misunderstood part of statistics. Entire papers have been written about how researchers misinterpret p-values, how significance thresholds are arbitrary, and how the multiple testing problem inflates false discoveries. Understanding both the theory and the pitfalls is essential for anyone who works with data.&lt;/p></description></item></channel></rss>