Experiment Designer

Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.

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. 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.

Output Structure

[Design or Results header based on phase]

Hypothesis: "If we [change], we expect [metric] to [move by X%] because [reason]"

Primary metric: [One metric only] Guardrail metrics: [2-3 max] Required sample size: [n per variant] Estimated run time: [days] Pre-defined success threshold: [specific number] Design risk flags: [any concerns]

Results (Phase 2 only): Statistical significance: [p-value and conclusion] Practical significance: [lift size vs. business threshold] Recommendation: Ship / Iterate / Kill / Follow-up — [rationale]

Quality Checks

• Hypothesis specifies the change, the metric, the direction, and the reason
• Primary metric is singular — guardrail metrics are secondary
• Success criteria are defined before the test launches (not after seeing results)
• Test was not stopped early (or flagged clearly if it was)
• Practical significance assessed separately from statistical significance
• Sample ratio mismatch is checked in results interpretation

Anti-Patterns

• Do not define success criteria after seeing preliminary results — post-hoc success definitions are HARKing (Hypothesising After Results are Known) and invalidate the experiment
• Do not stop a test early because the result looks significant — early stopping dramatically inflates false positive rates; the test must run to the planned sample size
• Do not treat statistical significance as the same as practical significance — a p < 0.05 result with a 0.1% lift is real but may not be worth shipping
• Do not run the same experiment on the same population multiple times without correction — multiple testing inflates the chance of a false positive proportionally
• Do not use more than one primary metric — multiple primary metrics require multiple hypothesis corrections and make the ship/kill decision ambiguous