TLDR Tech / Data
A/B Testing Pitfalls: What Works and What Doesn't with Real Data
A/B testing failures are far more often caused by broken infrastructure and poor experimentation practices than by the ideas being tested. Common failures include Sample Ratio Mismatch (SRM) from bad randomization, early peeking that inflates false positives, insufficient statistical power, and optimizing the wrong metrics without guardrails, causing misleading results.
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