experiment-designer

# Experiment Designer Skill Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist. ## Required Inputs Ask the user for these if not provided: **For experiment design:** - Hypothesis (what change, what metric, what expected movement) - Current baseline metric value - Minimum detectable effect (MDE) — the smallest lift worth caring about - Available daily sample size **For results interpretation:** - Control and variant results (raw numbers or percentages) - P-value or confidence interval - Run duration (days) - Any anomalies observed during the test ## Two-Phase Process ### Phase 1: Experiment Design 1. Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]" 2. Define control and variant clearly 3. Select primary metric (one only) and secondary guardrail metrics (2-3 max) 4. Calculate required sample size from MDE and baseline 5. Estimate run time in days 6. Set pre-defined success criteria before the test runs — no moving goalposts 7. Flag design risks: novelty effects, seasonal confounds, multiple testing issues, network effects, sample ratio mismatch ### Phase 2: Results Interpretation 1. Assess statistical significance (p < 0.05 threshold) 2. Assess practical significance: was the lift meaningful for the business, not just real? 3. Interpret confidence intervals 4. Investiga...