measure-experiment-results

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 --> # Experiment Results An experiment results document captures what happened when you tested a hypothesis, including statistical outcomes, segment analysis, learnings, and clear recommendations. Good results documentation turns individual experiments into organizational knowledge that improves future decision-making. ## When to Use - After an A/B test or experiment reaches statistical significance - When an experiment is ended early (for any reason) - To communicate findings to stakeholders who weren't involved - During decision-making about whether to ship, iterate, or kill a feature - To build a repository of learnings that inform future experiments ## When NOT to Use - The experiment is not designed or run yet -> use `measure-experiment-design` - The results demand a direction decision -> use `iterate-pivot-decision`; this skill reports the evidence, that one decides - You want the transferable learning banked for the organization -> follow up with `iterate-lessons-log` - Your data is survey responses, not a controlled experiment -> use `measure-survey-analysis` ## Instructions When asked to document experiment results, follow these steps: 1. **Summarize the Experiment** Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists. 2. **Restate the Hypothesis** Remind readers what you believed would happen and why. This frames the results interpretation. 3. **Present Primary Results** Show the primary metric outcome clearly: what were the values for control and treatment? Include statistical significance (p-value), confidence intervals, and sample sizes. Be honest about whether results are conclusive. 4. **Analyze Secondary Metrics** Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative. 5. **Segment the Data** Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights. 6. **Extract Learnings** What did you learn beyond the numbers? Include surprising findings, questions raised, and implications for the product hypothesis. Negative results are valuable learnings. 7. **Make a Recommendation** Be clear: should we ship, iterate, or kill? Support the recommendation with the evidence. If the decision is nuanced, explain the trade-offs. 8. **Define Next Steps** Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update. ## Output Format Use the template in `references/TEMPLATE.md` to structure the output. A complete readout fills every template section: Summary; Hypothesis Recap; Results; Segment Analysis; Visualization; Learnings; Recommendation; Next Steps; and Appendix. ## Quality Checklist Before finalizing, verify: - [ ] Statistical methods and significance are clearly stated - [ ] Confidence intervals are included (not just p-values) - [ ] Segment analysis checked for differential effects - [ ] Secondary/guardrail metrics are reported - [ ] Learnings go beyond just the numbers - [ ] Recommendation is clear and actionable - [ ] Negative or inconclusive results are reported honestly ## Examples See `references/EXAMPLE.md` for a completed example.