A/B Testing Pitfalls: What Works and What Doesn’t with Real Data

Image by Author # Introduction You've shipped what looks like a winning test: conversion up 8%, engagement metrics glowing green. Then it crashes in production or quietly fails a month later. If that sounds familiar, you're not alone. Most A/B test failures don't come from bad product ideas; they come from bad experimentation practices. The data misled you, the stopping rule was ignored, or no one checked if the "win" was just noise dressed as a signal. Here's the uncomfortable truth: the infrastructure around your test matters more than the variant itself, and most teams get it wrong. Let's break down the four silent killers of A/B testing — from misleading data to flawed logic — and reveal the disciplined practices that separate the best from the rest.

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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets.