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:
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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.
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Restate the Hypothesis Remind readers what you believed would happen and why. This frames the results interpretation.
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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.
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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.
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Segment the Data Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.
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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.
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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.
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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.