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Experiment Designer

Product experiment-designer Source

Install: claude /plugin install product-skills

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions.

When To Use

Use this skill for: - A/B and multivariate experiment planning - Hypothesis writing and success criteria definition - Sample size and minimum detectable effect planning - Experiment prioritization with ICE scoring - Reading statistical output for product decisions

Core Workflow

  1. Write hypothesis in If/Then/Because format
  2. If we change [intervention]
  3. Then [metric] will change by [expected direction/magnitude]

  4. Because [behavioral mechanism]

  5. Define metrics before running test

  6. Primary metric: single decision metric
  7. Guardrail metrics: quality/risk protection

  8. Secondary metrics: diagnostics only

  9. Estimate sample size

  10. Baseline conversion or baseline mean
  11. Minimum detectable effect (MDE)
  12. Significance level (alpha) and power

Use: 

python3 scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute

  1. Prioritize experiments with ICE
  2. Impact: potential upside
  3. Confidence: evidence quality
  4. Ease: cost/speed/complexity

ICE Score = (Impact * Confidence * Ease) / 10

  1. Launch with stopping rules
  2. Decide fixed sample size or fixed duration in advance
  3. Avoid repeated peeking without proper method

  4. Monitor guardrails continuously

  5. Interpret results

  6. Statistical significance is not business significance
  7. Compare point estimate + confidence interval to decision threshold
  8. Investigate novelty effects and segment heterogeneity

Hypothesis Quality Checklist

  • Contains explicit intervention and audience
  • Specifies measurable metric change
  • States plausible causal reason
  • Includes expected minimum effect
  • Defines failure condition

Common Experiment Pitfalls

  • Underpowered tests leading to false negatives
  • Running too many simultaneous changes without isolation
  • Changing targeting or implementation mid-test
  • Stopping early on random spikes
  • Ignoring sample ratio mismatch and instrumentation drift
  • Declaring success from p-value without effect-size context

Statistical Interpretation Guardrails

  • p-value < alpha indicates evidence against null, not guaranteed truth.
  • Confidence interval crossing zero/no-effect means uncertain directional claim.
  • Wide intervals imply low precision even when significant.
  • Use practical significance thresholds tied to business impact.

See: - references/experiment-playbook.md - references/statistics-reference.md

Tooling

scripts/sample_size_calculator.py

Computes required sample size (per variant and total) from: - baseline rate - MDE (absolute or relative) - significance level (alpha) - statistical power

Example: 

python3 scripts/sample_size_calculator.py \
  --baseline-rate 0.10 \
  --mde 0.015 \
  --mde-type absolute \
  --alpha 0.05 \
  --power 0.8