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 ## 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 a...