<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scientific Method on Aneesh Sathe</title><link>https://aneeshsathe.com/tags/scientific-method/</link><description>Recent content in Scientific Method on Aneesh Sathe</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 22 Jul 2025 06:32:55 +0000</lastBuildDate><atom:link href="https://aneeshsathe.com/tags/scientific-method/index.xml" rel="self" type="application/rss+xml"/><item><title>My Road to Bayesian Stats</title><link>https://aneeshsathe.com/my-road-to-bayesian-stats/</link><pubDate>Tue, 22 Jul 2025 06:32:55 +0000</pubDate><guid>https://aneeshsathe.com/my-road-to-bayesian-stats/</guid><description>&lt;p&gt;By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.&lt;/p&gt;</description></item></channel></rss>