Measure Experiment Results

After you run an A/B test or experiment, this helps you document what actually happened with proper statistical rigor. It walks you through presenting primary and secondary metrics, analyzing results by segment, extracting learnings beyond the numbers, and making a clear ship/iterate/kill recommendation. The quality checklist pushes you to include confidence intervals and honestly report negative results, which is refreshing. Most teams are terrible at turning individual experiments into organizational knowledge, so having a structured way to capture not just outcomes but learnings and next steps is legitimately useful for building institutional memory.

skills.sh installs: 787
GitHub stars: 794
last updated: Aug 21, 2026

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Install

$ npx -y skills add product-on-purpose/pm-skills --skill measure-experiment-results --agent claude-code

Installs into .claude/skills of the current project.

Files

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