A/B Testing Framework
CautionDesign and implement A/B tests with proper statistical methodology, sample size calculation, feature flags, and significance testing for conversion optimization.
$ Copy the SKILL.md file to .claude/skills/a-b-testing.md About This Skill
A/B Testing Framework generates statistically rigorous experimentation infrastructure to avoid the common mistakes that invalidate most A/B tests.
Pre-Experiment Design
Sample size calculator with inputs: baseline conversion rate, minimum detectable effect (MDE), statistical power (80% default), and significance threshold (α=0.05). Runtime estimator based on current traffic volume. Multiple comparison correction (Bonferroni) for multi-variant tests.
Assignment
Deterministic user bucketing via MurmurHash3 on `user_id + experiment_id`. Ensures users see the same variant on every visit. Traffic allocation by percentage. Holdout groups for long-term effect measurement. Exclusion rules to prevent experiment interference.
Feature Flags
Integrates with LaunchDarkly, Unleash, or a self-hosted flag service. Server-side flag evaluation prevents flickering. SDK wrappers for React (useFlag hook), Python, and Go.
Analysis
Frequentist — Z-test for proportions, t-test for continuous metrics, chi-square for multi-category. Confidence intervals. p-value with multiple testing correction.
Bayesian — Beta-Binomial conjugate model for conversion rates. Probability to be best, expected loss, credible intervals. Thompson Sampling for multi-armed bandit scenarios.
Common Pitfalls Detection
Sample Ratio Mismatch (SRM) detection, novelty effect warnings for long-run test drift, and network effect warnings for social products.
Use Cases
- Running landing page copy tests with proper power analysis and minimum detectable effect
- Implementing feature flag-based A/B tests with consistent user bucketing
- Analyzing experiment results with frequentist and Bayesian methods
- Designing multi-variate tests with proper traffic allocation across variants
Pros & Cons
Pros
- +Pre-experiment sample size calculator prevents underpowered tests
- +SRM detection catches assignment bugs that would otherwise invalidate results
- +Bayesian analysis provides probability-based decisions, not just p-value cutoffs
- +MurmurHash bucketing ensures consistent assignment without database storage
Cons
- -Minimum sample sizes mean small sites cannot reach significance on rare conversions
- -Bayesian analysis requires choosing priors which introduces subjective decisions
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