<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data on Mark Aron Szulyovszky</title><link>https://almostintuitive.com/docs/data/</link><description>Recent content in Data on Mark Aron Szulyovszky</description><generator>Hugo -- gohugo.io</generator><language>en-US</language><atom:link href="https://almostintuitive.com/docs/data/index.xml" rel="self" type="application/rss+xml"/><item><title>Observational studies</title><link>https://almostintuitive.com/docs/data/observational-studies/</link><pubDate>Mon, 20 Jul 2020 00:00:00 +0000</pubDate><guid>https://almostintuitive.com/docs/data/observational-studies/</guid><description>Why you probably shouldn&amp;rsquo;t trust observational studies 1. Undersampling failure Let&amp;rsquo;s say you&amp;rsquo;re doing research on why certain companies are more successful than others. Your data set only contains successful companies. You&amp;rsquo;ll have a lot of them taking huge risks, that combined with luck created runaway success. And you completely ignored the companies who took huge risks, but weren&amp;rsquo;t lucky enough, and ended up being bankrupt. Your sample naturally overweight the unreasonable risk takers.</description></item></channel></rss>