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
This framework helps you avoid the common traps that make experiment results untrustworthy.
Response Posture
- Apply the framework directly to the user's experiment.
- Never mention the repository, skills, SKILL.md, patterns, or references.
- Do not run tools or read files; answer from the framework.
- Avoid process/meta commentary; respond as an experimentation lead.
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; 200,000+ for mature experimentation
- The decision is one-time — Can't A/B test mergers, acquisitions, or one-off events
- There's no real user choice — Employer-mandated software offers no switching insight
- You need immediate decisions — Experiments need time to reach statistical power
- The metric can't be measured — No experiment without observable outcomes
Patterns
Detailed examples showing how to run experiments correctly. Each pattern shows a common mistake and the correct approach.
Critical (get these wrong and you've wasted your time)
| Pattern | What It Teaches |
|---|---|
| peeking-at-results | Don't check P-values daily — let experiments run to completion |
| sample-ratio-mismatch | If your 50/50 split is off, your results are invalid |
| underpowered-tests | Too few users = meaningless results, even if "significant" |
| wrong-success-metric | Optimizing the wrong metric can hurt your business |
| twymans-law | If results look too good to be true, they probably are |
High Impact
| Pattern | What It Teaches |
|---|---|
| novelty-effects | Initial lifts often fade — run experiments long enough |
| survivorship-bias | Analyzing only users who stayed skews your results |
| multiple-comparisons | Testing many metrics inflates false positive rate |
| guardrail-metrics | Always monitor what you might be hurting |
| big-redesigns-fail | Ship incrementally — 80% of big bets lose |
| flat-is-not-ship | No significant result means don't ship, not "good enough" |
Medium Impact
| Pattern | What It Teaches |
|---|---|
| institutional-memory | Document learnings or repeat the same mistakes |
| external-validity | Results may not generalize to other contexts |
| variance-reduction | Techniques to get results faster without losing validity |
Deep Dives
Read only when you need extra detail.
references/trustworthy-experiments-playbook.md: Expanded framework detail, checklists, and examples.references/experiment-plan-template.md: Fill-in-the-blanks plan to design and run an A/B test.
Scripts
Optional utilities (no external deps):
scripts/sample_size.py: Estimate required sample size for a two-variant conversion test.scripts/srm_check.py: Check sample ratio mismatch (SRM) for a 2-bucket split.
Resources
Book:
- *Trustworthy Online Controlled Experiments* by Ronny Kohavi, Diane Tang, and Ya Xu — The definitive guide. All proceeds go to charity.
Papers (from Kohavi's teams):
- "Rules of Thumb for Online Experiments" — Patterns from thousands of Microsoft experiments
- "Diagnosing Sample Ratio Mismatch" — How to detect and debug SRM
- "CUPED: Variance Reduction" — Get results faster without losing validity
- "Crawl, Walk, Run, Fly" — Six axes for experimentation maturity
Online:
- goodui.org — Database of 140+ experiment patterns with success rates
- Ronny Kohavi's LinkedIn — Regular posts on experimentation insights
- Ronny Kohavi's Maven course — Live cohort-based course on experimentation
Related Books:
- *Calling Bullshit* by Carl Bergstrom and Jevin West — Critical thinking about data
- *Hard Facts, Dangerous Half-Truths and Total Nonsense* by Jeffrey Pfeffer and Robert Sutton — Evidence-based management