Agent skill · data analytics · phuryn
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 a variant.
Why this skill is useful
Generates Python scripts for statistical calculations and provides domain-specific analysis procedures for A/B testing.
What it needs
Requires python installed locally. About 2k tokens when loaded. Last updated 2026-07-03. 24,941 stars on the source repository.
What this skill does
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)? Novelty/primacy effects: Was there enough time to wash out initial behavior changes? 3. Calculate statistical significance: Conversion rate for control and variant Relative lift: (variant - control) / control × 100 p-value: Using a two-tailed z-test or chi-squared test Confidence interval: 95% CI for the difference Statistical significance: Is p < 0.05? Practical significance: Is the lift meaningful for the business? If the user provides raw data, generate and run a Python script to calculate these. 4. Check guardrail metrics: Did any guardrail metrics (revenue, engagement, page load time) degrade? A winning primary metric with degraded guardrails may not be a true win 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills phuryn/ab-test-analysis