ab-test-planner
GitHub用于设计统计严谨的A/B测试，涵盖假设制定、样本量计算、持续时间估算及结果解读。提供完整测试计划模板，确保实验具备方向性假设、明确指标及回滚方案，保障数据可信度。
Trigger Scenarios
Install
npx skills add mohitagw15856/pm-claude-skills --skill ab-test-planner -g -y
SKILL.md
Frontmatter
{
"name": "ab-test-planner",
"homepage": "https:\/\/mohitagw15856.github.io\/pm-claude-skills\/skill\/ab-test-planner.html",
"metadata": {
"openclaw": {
"emoji": "🚚"
}
},
"description": "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.
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
Scoring Rubric (0–40)
Score any output of this skill before handing it over; 32+ is ship-quality.
| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Statistical rigour | No sample size, or a number with no stated baseline/MDE behind it | Sample size present but MDE is guessed or copied from the lookup table without checking the actual baseline; power/significance unstated | Sample size derived from the stated baseline and MDE at 80% power / 95% confidence, duration checked against real daily traffic and the 2–4 week window, and the low-traffic escape hatch invoked if it doesn't fit |
| Hypothesis discipline | "Let's see what happens" — no direction, no magnitude, or multiple changes bundled into one variant | Directional hypothesis but missing magnitude, segment, or the evidence-based because; variant purity not confirmed | Full template filled (change, metric, direction, magnitude, segment, rationale citing data), and the treatment isolates exactly one change with excluded ideas named as follow-up tests |
| Guardrails & rollback | No guardrail metrics, or a rollback line with no threshold | Guardrails named but denominators/definitions ambiguous; rollback trigger vague ("if things look bad") | 1–2 guardrails protecting revenue or core engagement with pre-agreed definitions, concrete rollback thresholds, and the peeking-vs-harm-monitoring distinction handled explicitly |
| Decision readiness | No interpretation guide; results will be argued about after the fact | Ship/iterate/reject listed but thresholds fuzzy; inconclusive outcome missing or treated as a soft win | All four outcomes (ship / iterate / reject / inconclusive) mapped to pre-committed thresholds, including what an inconclusive result costs and what each outcome changes next |
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
Version History
- 54fad50 Current 2026-07-19 12:09
