pm-claude-skills/exports/windsurf/pm-delivery/ab-test-planner/ab-test-planner.md

trigger, description

trigger	description
model_decision	Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.

A/B Test Planner Skill

Design experiments that produce trustworthy results — not just directional signals. Every test output includes hypothesis, success metrics, sample size, duration, and a results interpretation guide.

Required Inputs

Ask the user for these if not provided:

• What is being tested (feature, UI change, copy, pricing, onboarding step)
• Hypothesis (or ask to help formulate one)
• Primary metric (conversion rate, click-through, completion rate, etc.)
• Baseline rate and minimum detectable effect (MDE)
• Daily eligible users (to calculate duration)

Experiment Design Checklist

Before running any test, confirm:

• Clear hypothesis with predicted direction
• Single primary metric (plus up to 2 guardrail metrics)
• Minimum detectable effect (MDE) defined
• Sample size calculated
• Test duration estimated
• Segment isolated (no overlap with other running tests)
• Rollback plan defined

Hypothesis Template

"We believe that [change] will cause [primary metric] to [increase/decrease] by [X%] for [user segment], because [rationale based on data or insight]."

Never run a test without a directional hypothesis. "Let's just see what happens" is not a hypothesis.

Sample Size Calculator Logic

Use this formula (provide the output, not the formula, to the user):

• Baseline conversion rate: Current rate of primary metric
• MDE: Smallest change worth detecting (recommend 10–20% relative lift for most features)
• Statistical power: 80% (standard)
• Significance level: 95% (p < 0.05)

For common scenarios, provide pre-calculated estimates:

Baseline Rate	MDE (Relative)	Required Sample per Variant
5%	20%	~19,000
10%	15%	~14,000
20%	10%	~15,000
40%	10%	~9,500
60%	5%	~42,000

Always warn: "These are estimates. Use a tool like Evan Miller's calculator or Statsig for precision."

Test Duration Guidance

Minimum: 2 full weeks (to capture weekly seasonality) Maximum: 4 weeks (novelty effect distorts results beyond this)

Duration = Required sample ÷ (Daily traffic × % exposed)

Flag if traffic is too low to reach significance in under 8 weeks — recommend a different approach (e.g., holdout test, qualitative research).

Output Format

A/B Test Plan — [Test Name] — [Date]

Hypothesis:

[Filled hypothesis template]

Variants:

• Control (A): [Current experience]
• Treatment (B): [Changed experience — be specific]

Primary Metric: [Metric name + how measured] Guardrail Metrics: [Metrics that must not degrade]

Target Segment: [Who sees the test — % of traffic, user type] Traffic Split: [50/50 recommended unless ramp-up needed]

Sample Size Required: ~[N] users per variant Estimated Duration: [X] weeks (based on [Y] daily eligible users) Significance Threshold: 95% confidence, 80% power

Exclusions: [Any user segments to exclude and why]

Rollback Trigger: If [guardrail metric] degrades by [X%], stop the test immediately.

Results Interpretation Guide:

• ✅ Ship if: Treatment shows [X%]+ lift on primary metric at 95% confidence AND guardrail metrics are stable
• 🔄 Iterate if: Direction is positive but not significant — consider extending or redesigning
• ❌ Reject if: No lift or negative direction at significance
• ⚠️ Inconclusive: Do not ship. Do not call it a win.

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Guidelines

• Always recommend against peeking at results before the test reaches planned sample size — explain p-hacking risk
• If user wants to test multiple variants, explain the multiple comparisons problem and recommend a Bonferroni correction or a Bayesian approach
• If traffic is very low (<1,000 users/day), recommend qualitative alternatives: moderated testing, 5-second tests, or user interviews
• Never approve a test with no guardrail metrics — always protect revenue, retention, or core engagement

Anti-Patterns

• Do not run a test without a directional hypothesis — "let's see what happens" produces uninterpretable results
• Do not declare a winner before reaching the pre-planned sample size — peeking at results inflates false positive rates
• Do not test multiple independent changes in a single variant — you won't know which change caused the result
• Do not use engagement metrics (clicks, time-on-page) as the primary metric when the goal is revenue or retention — proxy metrics mislead
• Do not ignore guardrail metrics — a conversion lift that causes a support ticket spike is not a win

Quality Checks

• Hypothesis is directional (predicts a specific direction and magnitude, not "let's see")
• Primary metric is singular (guardrail metrics are secondary)
• Sample size is calculated from actual MDE and baseline (not guessed)
• Test duration accounts for weekly seasonality (minimum 2 weeks)
• Guardrail metrics are defined (at least one to protect revenue or core engagement)
• Rollback trigger is specified with a concrete threshold