Experiment designer

Skill alirezarezvani/claude-skills/product-team/skills/experiment-designer

345 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 330+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8 more coding agents — engineering, marketing, product, compliance, C-level advisory, research, business operations, commercial & finance, and your daily productivity skills.

Install

npx -y skills add alirezarezvani/claude-skills --skill experiment-designer

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What its author says it does

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Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.

SKILL.md

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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.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