Experiment Designer

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
   - If we change [intervention]
   - Then [metric] will change by [expected direction/magnitude]
   - Because [behavioral mechanism]

2. Define metrics before running test
   - Primary metric: single decision metric
   - Guardrail metrics: quality/risk protection
   - Secondary metrics: diagnostics only

3. Estimate sample size
   - Baseline conversion or baseline mean
   - Minimum detectable effect (MDE)
   - Significance level (alpha) and power

Use:

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

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

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

5. Launch with stopping rules
   - Decide fixed sample size or fixed duration in advance
   - Avoid repeated peeking without proper method
   - Monitor guardrails continuously

6. Interpret results
   - Statistical significance is not business significance
   - Compare point estimate + confidence interval to decision threshold
   - 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