A/B Test Design and Results Readout
Plans A/B tests your traffic can actually support, sized and pre-registered, and checks finished tests for false wins before you ship.
Before & after
Same prompt, same agent — the only difference is the skill.
A/B testing checkout sounds like a good idea. You'd want to run different versions of the page and see which converts better. The most common things to test are like the button color, copy, or number of form fields. You'd need to make sure you have enough traffic to get statistical significance. It depends on your current conversion rate and how many visitors you get. What version are you thinking of testing, and how long do you want to run it?
A/B TEST DESIGN: Mobile Checkout Conversion HYPOTHESIS "Reducing form fields from 8 to 5 on mobile checkout will increase completion rate by 8% (from 62% to 67%)" Logic: Drop optional upsell questions + address verification (auto-fill via ZIP + country) to reduce cognitive load Risk: Might lose shipping accuracy on first-time users if address validation too permissive TEST PARAMETERS Metric: Completion rate (orders placed / checkouts started) on mobile only (≤768px) Current baseline: 62% (measured last 30 days) Effect size target: 8% relative lift (62% → 67%) Statistical power: 80% (standard) Significance threshold: p<0.05 SAMPLE SIZE CALCULATOR • Current CVR: 62%, Baseline traffic: 8,000 mobile checkouts/month • Daily mobile traffic: 267 checkouts • Minimum sample per variant: 2,900 (need 43 days at 50/50 split) • Safe duration: 50 days to reach 95% confidence CONTROL VARIANT (A) 8 fields: email, shipping address, city, state, ZIP, country, phone, promo code Button label: "Place Order" Estimated form completion time: 90 seconds (from interaction analytics) TREATMENT VARIANT (B) 5 fields: email, ZIP (triggers address lookup), country, phone, promo code Auto-fill: Address pulled from ZIP + country (using postal API) Button label: "Complete Order" (minor copy test) Estimated form completion time: 45 seconds SUCCESS CRITERIA (All must pass to declare winner) ✓ Treatment CVR > 67% (statistically significant vs 62% baseline) ✓ Error rate (invalid addresses) <3% (ensure address lookup didn't break deliveries) ✓ Customer support impact: chargeback/address disputes flat or declining ✓ Checkout abandonment at address step <5% (users don't get stuck on auto-fill) FAILURE MODE: Declare no winner if ✗ Treatment CVR = 64% (lifted, but not 8% target, might be random noise) ✗ Error rate jumps to 8% (address accuracy trade-off not worth 5% CVR gain) ✗ Support tickets for "wrong address shipped" spike 40% (hidden cost) TIMELINE Days 1–2: QA both variants, code review Day 3: Deploy at 50/50 split to 5% of mobile traffic (test 1,350 users), monitor dashboards Days 4–8: Ramp to 50% mobile traffic if no critical issues (13,500 users in test) Days 9–50: Run to completion (2,900 per variant) Day 51: Analysis, decision, rollout if winner declared SAFETY RAILS • Kill switch: If error rate >5% immediately revert to A (automated alert at 2%) • Monitoring: Watch revenue per session, support tickets, payment declined rate daily • Holdout: Keep 5% of traffic on control to detect drift over 50 days IMPORTANT: Results are illustrative sample numbers. Actual effect depends on your traffic, existing CVR, and user base. This is not financial or conversion advice.
About this skill
Your tests run for weeks and never reach significance, or a "winner" ships and revenue doesn't move. This skill does the math before you build anything. It checks whether your traffic can detect the lift you care about, fixes the metric and stopping rule before launch, and checks finished tests for broken plumbing before anyone celebrates.
What it does
- Calculates sample size and run time from your baseline rate, the smallest lift worth shipping, and your eligible traffic, and shows the arithmetic
- Tells you plainly when a test can't work at your traffic, and offers fixes: a bolder change, an upstream metric, pooled pages, or ship-and-monitor
- Writes a pre-registered plan: a three-part hypothesis, one primary metric, guardrails with thresholds, randomization unit, exposure trigger, and a decision rule
- Audits pasted results for sample ratio mismatch, peeking, short durations, novelty decay, and multiple-comparison false positives
- Reports lift as a confidence interval with a clear ship, keep-control, or rerun call
- Writes assignment and analysis code inline when you need it
What you get
A one-page test plan with every number computed and every assumption labeled. For finished tests, you get a readout that checks integrity first, then reports the effect, guardrails, and decision.
Best for
Growth marketers, product managers, CRO specialists, and founders running landing page, checkout, pricing, onboarding, or email experiments. Especially useful without a dedicated data scientist on hand.
SKILL.md preview
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name: ab-test-design-and-readout
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