- name
- experiment-designer
- description
- Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
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
- Write hypothesis in If/Then/Because format
- If we change
[intervention] - Then
[metric]will change by[expected direction/magnitude] - Because
[behavioral mechanism]
- Define metrics before running test
- Primary metric: single decision metric
- Guardrail metrics: quality/risk protection
- Secondary metrics: diagnostics only
- 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- Prioritize experiments with ICE
- Impact: potential upside
- Confidence: evidence quality
- Ease: cost/speed/complexity
ICE Score = (Impact * Confidence * Ease) / 10
- Launch with stopping rules
- Decide fixed sample size or fixed duration in advance
- Avoid repeated peeking without proper method
- Monitor guardrails continuously
- 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.mdreferences/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