Run A/B tests and hypothesis tests
statistical-analystpluginsetup L2★17,464
alirezarezvani/claude-skills ↗What it does
Run hypothesis tests, A/B analyses, and power calculations
Best for
Validating A/B experiment results with rigorous statistics instead of eyeballing conversion rates or running tests that are too small
Inputs
- · sample data
- · effect size
- · confidence level
Outputs
- · test result (p-value, confidence interval)
- · power analysis
- · sample size recommendation
Requires
- · 3 stdlib Python tools (Z-test, t-test, chi-square, effect sizes, power analysis, Wilson score intervals)
Preconditions
Data collected from experiment or observational study; hypothesis formulated before test
Failure modes
Multiple-comparison bias (many tests, high false-positive rate); low power (experiment underpowered for real effect); wrong test chosen (parametric vs. non-parametric)
Trust signals
- · 3 stdlib-only Python tools
- · Covers Z-test, t-test, chi-square, effect sizes, power analysis, Wilson score intervals
- · v2.9.0 stable release