{"repo":"AmirhosseinHonardoust/Trend-vs-Collection-Forensics","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Trend-vs-Collection-Forensics","clone":"git clone https://github.com/AmirhosseinHonardoust/Trend-vs-Collection-Forensics.git","description":"A deep-dive analytics article on why most KPI “trends” are actually measurement artifacts. Covers the fake-trend taxonomy (schema drift, coverage loss, time shifts, backfills, dedupe, sampling, mix change), a Trend Courtroom evidence protocol, segment invariance tests, and copy-ready checklists + a Trend Report Card for decision safety.","language":null,"stars":10,"topics":["analytics","cohort-analysis","coverage","dashboards","data-analysis","data-engineering","data-quality","data-science","decision-making","event-tracking"],"license":"MIT","category":"dashboards-admin","readme_excerpt":"You Don’t Have a Trend, You Have a Data Collection Problem Most “trends” don’t come from user behavior. They come from measurement changing quietly . And the reason this hurts so much is simple: when dashboards move, humans need a story. We invent one. We pick the most plausible narrative, attach a clean arrow, and ship a conclusion. That’s how you end up celebrating a KPI increase that was actually: - an iOS tracking bug, - a schema drift that turned “missing” into “zero,” - a timezone shift that moved events into the next day, - a pipeline dedupe update that removed duplicates and “reduced engagement,” - a backfill that created a false surge, - or a sampling change that removed low-quality traffic and “improved conversion.” This article is a practical guide to prevent that. Not philosophy. Not “data best practices.” This is analytics forensics : how to prove a trend is real before you let it touch a decision. --- The central idea: Trends are guilty until proven innocent A KPI chart is a claim. And every claim needs evidence. What you think you have - “DAU is down 8%.” - “Retention improved after the new onboarding.” - “Conversion jumped this week.” - “Average order value is falling.” What you might actually have - “We stopped collecting events from one platform.” - “A logging definition changed.” - “A property became null for a cohort.” - “Late events shifted into the next day.” - “A bot filter removed traffic that used to inflate the denominator.” - “A pipeline step starte","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Trend-vs-Collection-Forensics/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}