Plan and analyze A/B tests statistically
experiment-designerskillsetup L2★17,464
alirezarezvani/claude-skills ↗What it does
Design, prioritize, and evaluate product experiments with statistical rigor
Best for
Product teams running defensible experiments with clear success criteria and statistical stopping rules.
Inputs
- · Hypothesis (If/Then/Because), metrics (primary/guardrail/secondary), baseline conversion/mean
Outputs
- · Sample size estimate, ICE prioritization score, stopping rules, result interpretation guide
Requires
- · python3 scripts/sample_size_calculator.py
Preconditions
Clear intervention target; baseline metrics available
Failure modes
- · Hypothesis too vague
- · MDE unrealistic
- · Repeated peeking without correction
Trust signals
- · If/Then/Because hypothesis template
- · Primary vs guardrail metric separation
- · ICE scoring for prioritization