By name: measure-experiment-results description: 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. license: Apache-2.0 metadata: phase: measure version: "2.1.0" updated: 2026-06-10 category: reflection frameworks: [triple-diamond, lean-startup, design-thinking] author: product-on-purpose
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:
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.
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.