Design an A/B Test for Homepage Layout Impact
Experiment Design: New Homepage Layout → Purchase Rate
Context
You are designing an A/B test to evaluate whether a new homepage layout increases purchase rate for a high-traffic consumer website. The homepage is often the first touchpoint and changes may affect both conversion and user experience.
Task
Describe how you would design the experiment to measure the impact of the new layout on purchase rate. Address the following:
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Population and exposure criteria (inclusions/exclusions)
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Metrics
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Primary success metric
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Secondary/diagnostic metrics
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Guardrail metrics
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Randomization strategy
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Unit of randomization, bucketing, consistency
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Sample size and duration
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Power analysis and minimum detectable effect (MDE)
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Test duration and ramp-up plan
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Bias control and validity threats
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Key risks and mitigations (e.g., SRM, interference, novelty, seasonality)
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Analysis plan
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Estimand, statistical tests, confidence intervals
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Segmentation/heterogeneity analysis
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Multiple testing and decision criteria
Include explicit assumptions as needed. Mention success metric, guardrails, power analysis, segmentation, and validity threats.
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
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State assumptions about instrumentation, randomization, sample size, and data quality.
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Separate descriptive analysis from causal claims.
What a Strong Answer Covers
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A metric framework with primary, guardrail, and diagnostic metrics.
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A credible analysis or experiment design with clear assumptions and bias checks.
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SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
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An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions
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What sanity checks would you run before trusting the result?
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How would you handle novelty effects, seasonality, or selection bias?
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What decision would you make if metrics disagree?