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From pm-skills
Documents completed experiment or A/B test results: statistical analysis, learnings, and recommendations. Use after experiments to communicate findings, inform decisions, and build organizational knowledge.
npx claudepluginhub product-on-purpose/pm-skills --plugin pm-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/pm-skills:measure-experiment-resultsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
Summarizes A/B test results, declares a winner or inconclusive, and drafts stakeholder recommendations. Use after experiments complete for analysis and ship/kill decisions.
Analyzes A/B tests and experiments with statistical rigor: assesses power, significance, validity, segments; recommends ship/kill/extend.
Analyzes A/B test result CSV/table data and outputs PM-ready report with conclusion, results table, guardrail checks, bias/novelty warnings, and ship/iterate/kill recommendation. Always checks statistical significance vs. business meaning, guardrail violations, and p-hacking signals.
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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 asked to document experiment results, follow these steps:
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.
Restate the Hypothesis Remind readers what you believed would happen and why. This frames the results interpretation.
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.
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.
Segment the Data Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.
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.
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.
Define Next Steps Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update.
Use the template in references/TEMPLATE.md to structure the output.
Before finalizing, verify:
See references/EXAMPLE.md for a completed example.