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. 1. Write hypothesis in If/Then/Because format

• - If we change INLINECODE0
• Then [metric] will change by INLINECODE2
• Because INLINECODE3

1. 2. Define metrics before running test

• - Primary metric: single decision metric
• Guardrail metrics: quality/risk protection
• Secondary metrics: diagnostics only

1. 3. Estimate sample size

• - Baseline conversion or baseline mean
• Minimum detectable effect (MDE)
• Significance level (alpha) and power

Use:
CODEBLOCK0

1. 4. Prioritize experiments with ICE

• - Impact: potential upside
• Confidence: evidence quality
• Ease: cost/speed/complexity

ICE Score = (Impact Confidence Ease) / 10

1. 5. Launch with stopping rules

• - Decide fixed sample size or fixed duration in advance
• Avoid repeated peeking without proper method
• Monitor guardrails continuously

1. 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:

• - INLINECODE4
• INLINECODE5

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:
CODEBLOCK1