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

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked (assumed, confirm) and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about). 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. Investigate confounding factors
5. Recommend: Ship / Iterate / Kill / Run follow-up test
6. Validate — Confirm the test ran for the full planned duration. Flag if it was stopped early (peeking problem). Confirm sample ratio mismatch did not occur.