reaperinvest A/B Test Plan
When to Use This Skill
Use this skill when you need to:
- Plan an A/B test for a landing page, email, ad, or feature
- Calculate required sample size and test duration
- Define clear success criteria before running an experiment
- Document test results and decide whether to implement the winner
DO NOT use this skill for multivariate tests with 5+ variables, scientific research experiments, or theoretical statistics exercises. This is for practical business A/B testing.
Core Principle
EVERY TEST MUST HAVE A HYPOTHESIS WRITTEN BEFORE THE TEST STARTS — IF YOU CANNOT STATE WHAT YOU EXPECT AND WHY, YOU ARE GUESSING, NOT TESTING.
Phase 1: Brief
Required Inputs
| Input | What to Ask | Default |
|---|---|---|
| What to test | "What are you testing? (headline, CTA, pricing page, email subject, ad creative)" | Must be provided |
| Current metric | "What is the current conversion rate or metric you want to improve?" | Must be provided or estimated |
| Goal | "What improvement would be meaningful? (e.g., +20% conversion rate)" | 10-20% relative improvement |
| Traffic/volume | "How much traffic or how many impressions does this asset get per week?" | Must be provided |
| Tool | "What testing tool will you use? (Google Optimize, VWO, Optimizely, built-in)" | Google Optimize or native platform |
| Risk tolerance | "How confident do you need to be? (90%, 95%, 99%)" | 95% statistical significance |
GATE: Confirm brief before proceeding.
Phase 2: Design
Hypothesis Framework
Write the hypothesis using this format:
"If we [change], then [metric] will [improve/increase/decrease] because [reason based on user behavior insight]."
Example: "If we change the CTA button text from 'Learn More' to 'Start Free Trial,' then click-through rate will increase by 15% because it sets a clearer expectation of the next step."
Test Design Elements
- Control (A) — current version, described specifically
- Variant (B) — changed version, with one clear difference
- Primary metric — the single metric that determines the winner
- Secondary metrics — supporting metrics to watch for unintended effects
- Sample size calculation — minimum visitors per variant
- Test duration — days to run based on traffic and sample size
- Segmentation — any audience segments to analyze separately
Sample Size Guidance
Provide the formula context:
- Baseline conversion rate + minimum detectable effect + significance level = required sample per variant
- Rule of thumb: at 5% baseline, detecting a 20% relative lift at 95% significance requires ~4,000 visitors per variant
GATE: Present the test plan and wait for approval.
Phase 3: Build
Deliverables
1. Complete Test Plan Document
- Hypothesis statement
- Control and variant descriptions with visual mockup notes
- Primary and secondary metrics
- Sample size and duration estimate
- Start and end dates
- Decision criteria (what score means "winner")
2. Pre-Launch Checklist
- Hypothesis documented
- Control and variant built and QA tested
- Tracking verified on both versions
- Traffic split configured (50/50 default)
- No other tests running on the same page/audience
- Minimum duration committed (do not peek early)
3. Results Documentation Template
- Variant performance table (metric, sample, conversion rate, confidence interval)
- Winner declaration with confidence level
- Recommendation: implement, iterate, or discard
- Learnings for future tests
Phase 4: Polish
Post-Test Analysis Framework
- Did it reach significance? If no, extend or call it inconclusive — never declare a winner below threshold.
- Check secondary metrics — did the winner hurt anything else? (e.g., more clicks but lower purchase rate)
- Segment analysis — did the variant win across all segments or only specific ones?
- Document the learning — even failed tests teach something. Record the insight.
Test Velocity Recommendation
Suggest a testing cadence: 1-2 tests per month for small businesses. Maintain a test backlog ranked by potential impact.
Example 1: Landing Page Headline Test
Hypothesis: Changing the headline from benefit-focused ("Save 10 Hours a Week") to pain-focused ("Stop Wasting 10 Hours on Tasks AI Can Handle") will increase signup rate by 15%. Duration: 3 weeks at 500 visitors/week. Primary metric: Email signup rate.
Example 2: Email Subject Line Test
Hypothesis: Adding the recipient's first name to the subject line will increase open rate by 10%. Duration: Single send to 5,000 subscribers, split 50/50. Primary metric: Open rate.
Anti-Patterns
- Testing without a hypothesis — random changes teach nothing. State your prediction and reasoning first.
- Peeking at results early — checking daily and stopping when it "looks good" inflates false positives. Commit to the duration.
- Testing too many variables — if you change the headline, image, AND CTA, you cannot attribute the result. One change per test.
- Tiny sample sizes — 50 visitors per variant proves nothing. Calculate the minimum before starting.
- Ignoring negative secondary metrics — a headline that gets more clicks but fewer purchases is not a winner.
Recovery
- Not enough traffic: Test higher-impact changes (bigger minimum detectable effect needs smaller sample). Or test in email/ads where volume is controllable.
- Test is inconclusive: Do not declare a winner. Either extend the test or accept that the difference is too small to matter and move on.
- Stakeholder wants to pick the winner by gut: Show the math. If results are not significant, implementing the "winner" is a coin flip.
- No testing tool budget: Use free tools (Google Optimize successor, email platform built-in splits) or manual URL splits with analytics tracking.