trustworthy-experiments

Domain Context This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work. Input Requirements Context about your product, feature, or problem Relevant data, research, or constraints (recommended but optional) Clear articulation of what you're trying to achieve Trustworthy Experiments What It Is Trustworthy Experiments is a framework for running controlled experiments (A/B tests) that produce reliable, actionable results. The core insight: most experiments fail, and many "successful" results are actually false positives. The key shift: Move from "Did the experiment show a positive result?" to "Can I trust this result enough to act on it?" Ronny Kohavi, who built experimentation platforms at Microsoft, Amazon, and Airbnb, found that: 66-92% of experiments fail to improve the target metric 8% of experiments have invalid results due to sample ratio mismatch alone When the base success rate is 8%, a P-value of 0.05 still means 26% false positive risk When to Use It Use Trustworthy Experiments when you need to: Design an A/B test that will produce valid, actionable results Determine sample size and runtime for statistical power Validate experiment results before making ship/no-ship decisions Build an experimentation culture at your company Choose metrics (OEC) that balance short-term gains with long-term value Diagnose why results look suspicious (Twyman's Law) Speed up experimentation without sacrificing validity When Not to Use It Don't use controlled experiments when: You don't have enough users — Need tens of thousands minimum The decision is one-time — Can't A/B test mergers or acquisitions There's no real user choice — Employer-mandated software You need immediate decisions — Experiments need time The metric can't be measured — No experiment without observable outcomes Resources Book: Trustworthy Online Controlled Experiments by Ronny Kohavi, Diane Tang, and Ya Xu