{"id":"786b0515-7c5f-4b88-b11f-b4e13a255d48","shortId":"3HjXyk","kind":"skill","title":"measure-experiment-results","tagline":"Documents the results of a completed experiment or A/B test with statistical analysis, learnings, and recommendations. Use after experiments conclude to communicate findings, inform decisions, and build organizational knowledge.","description":"<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->\n# Experiment Results\n\nAn 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.\n\n## When to Use\n\n- After an A/B test or experiment reaches statistical significance\n- When an experiment is ended early (for any reason)\n- To communicate findings to stakeholders who weren't involved\n- During decision-making about whether to ship, iterate, or kill a feature\n- To build a repository of learnings that inform future experiments\n\n## Instructions\n\nWhen asked to document experiment results, follow these steps:\n\n1. **Summarize the Experiment**\n   Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists.\n\n2. **Restate the Hypothesis**\n   Remind readers what you believed would happen and why. This frames the results interpretation.\n\n3. **Present Primary Results**\n   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.\n\n4. **Analyze Secondary Metrics**\n   Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative.\n\n5. **Segment the Data**\n   Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.\n\n6. **Extract Learnings**\n   What did you learn beyond the numbers? Include surprising findings, questions raised, and implications for the product hypothesis. Negative results are valuable learnings.\n\n7. **Make a Recommendation**\n   Be clear: should we ship, iterate, or kill? Support the recommendation with the evidence. If the decision is nuanced, explain the trade-offs.\n\n8. **Define Next Steps**\n   Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update.\n\n## Output Format\n\nUse the template in `references/TEMPLATE.md` to structure the output.\n\n## Quality Checklist\n\nBefore finalizing, verify:\n\n- [ ] Statistical methods and significance are clearly stated\n- [ ] Confidence intervals are included (not just p-values)\n- [ ] Segment analysis checked for differential effects\n- [ ] Secondary/guardrail metrics are reported\n- [ ] Learnings go beyond just the numbers\n- [ ] Recommendation is clear and actionable\n- [ ] Negative or inconclusive results are reported honestly\n\n## Examples\n\nSee `references/EXAMPLE.md` for a completed 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