<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Nikita Boyarkin — Writing (EN)</title><description>Articles on product analytics, A/B testing, and data science by Nikita Boyarkin.</description><link>https://nikitaboyarkin.github.io/en/</link><language>en-us</language><lastBuildDate>Thu, 01 Oct 2026 20:58:05 GMT</lastBuildDate><item><title>Why the aggregate lies: cohort retention triangles</title><link>https://nikitaboyarkin.github.io/en/posts/cohort-triangles-retention/</link><guid isPermaLink="true">https://nikitaboyarkin.github.io/en/posts/cohort-triangles-retention/</guid><description>Average retention across all cohorts mixes customers with different lifetimes and lies upward. How to build a triangular cohort matrix, read observation age, and compare cohorts honestly.</description><pubDate>Mon, 10 Aug 2026 00:00:00 GMT</pubDate><category>guide</category><category>retention</category><category>cohort-analysis</category><category>python</category><category>pandas</category></item><item><title>Calibrating A/B methods with simulation: how to verify a method before production</title><link>https://nikitaboyarkin.github.io/en/posts/ab-calibration-simulation/</link><guid isPermaLink="true">https://nikitaboyarkin.github.io/en/posts/ab-calibration-simulation/</guid><description>A p-value from a textbook doesn&apos;t prove a method — a simulation does. How to test every A/B module with an A/A check under the null and a power curve under the effect: CUPED, peeking, ratio metrics, mSPRT.</description><pubDate>Wed, 05 Aug 2026 00:00:00 GMT</pubDate><category>guide</category><category>ab-testing</category><category>statistics</category><category>python</category></item></channel></rss>