<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Statistics on Thoughts and code</title>
    <link>https://claydon.co/tags/statistics/</link>
    <description>Recent content in Statistics on Thoughts and code</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Thu, 02 Apr 2026 08:00:00 +0000</lastBuildDate><atom:link href="https://claydon.co/tags/statistics/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Z-Scores Over Gut Feel: Teaching Similarity Scores What “Weird” Actually Means</title>
      <link>https://claydon.co/code/its-just-vectors/part5-zscores/</link>
      <pubDate>Thu, 02 Apr 2026 08:00:00 +0000</pubDate>
      <guid>https://claydon.co/code/its-just-vectors/part5-zscores/</guid>
      <description>&lt;p&gt;A fixed similarity threshold is easy to implement and surprisingly hard to justify. It assumes your data behaves consistently, your model behaves consistently, and your definition of “normal” does not drift. None of those assumptions tend to hold for long.&lt;/p&gt;
&lt;p&gt;Up to this point in the series, we have treated cosine similarity as a raw signal. A number comes out, we compare it to a cutoff, and we move on. Here we start treating those numbers as a distribution instead.&lt;/p&gt;</description>
    </item>
    
  </channel>
</rss>
