/ab-test-analysis
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship
One skill from pm-skills.
shell
$ npx -y skills add phuryn/pm-skills --skill ab-test-analysis --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
How it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/ab-test-analysis
Context preview
The summary Claude sees to decide when to auto-load this skill.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship
Stats
Stars24,321
Forks2,503
LicenseMIT
SKILL.md
ab-test-analysis.SKILL.md
--- name: ab-test-analysis description: "Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant." --- ## A/B Test Analysis Evaluate A/B test results with statistical rigor and translate findings into clear product decisions. ### Context You are analyzing A/B test results for **$ARGUMENTS**. If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed. ### Instructions 1. **Understand the experiment**: - What was the hypothesis? - What was changed (the variant)? - What is the primary metric? Any guardrail metrics? - How long did the test run? - What is the traffic split? 2. **Validate the test setup**: - **Sample size**: Is the sample large enough for the expected effect size? - Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE² - Flag if the test is underpowered (<80% power) - **Duration**: Did the test run for at least 1-2 full business cycles? - **Randomization**: Any evidence of sample ratio mismatch (SRM)?