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