trustworthy-experiments

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Translated

Use when asked to "run an A/B test", "design an experiment", "check statistical significance", "trust our results", "avoid false positives", or "experiment guardrails". Helps design, run, and interpret controlled experiments correctly. Based on Ronny Kohavi's framework from "Trustworthy Online Controlled Experiments".

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npx skill4agent add pmprompt/claude-plugin-product-management trustworthy-experiments

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ab-testingexperimental-designstatistical-significancetrustworthy-experimentsmetric-selection

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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