Landing Page A/B Test Plan and Hypothesis Log
Plan an A/B test for a landing page with a clear hypothesis, sample size reasoning, run length, decision rule and results log.
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Help me design an A/B test for a landing page. Page and goal: [PAGE_AND_GOAL]. Current visitors per week and conversion rate: [TRAFFIC_AND_CONVERSION]. Change I want to test: [CHANGE_TO_TEST]. Tool used for testing: [TESTING_TOOL]. Risks of the change: [CHANGE_RISKS]. Produce: a hypothesis statement in the form "If we change X for audience Y, then metric Z will move because of reason R"; a test design table with the columns Item, Decision (variant count, traffic split, primary metric, secondary metrics, guardrail metrics, audience, start and end rule); an explanation in plain terms of why very small samples mislead; a rough sample size and duration estimate using a stated formula or a simple rule of thumb, showing the arithmetic with assumptions flagged and telling me to confirm with my tool's calculator [VERIFY]; a pre-launch quality check (tracking fires on both variants, page speed, mobile view); a decision rule written before the test starts including what to do on a flat result; and a log template with the columns Test ID, Hypothesis, Dates, Result, Learning, Next step. Warn about peeking at results early, running overlapping tests, and seasonal effects. Tool names and statistics settings differ, so mark them [VERIFY]. Do not claim any outcome is certain, and do not invent baselines or benchmarks. Add a worked example with invented figures: 2,000 visitors per week, a 3 percent conversion rate, a hoped-for lift to 3.6 percent, and a split into two equal groups, and estimate how long it may take.
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