This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design an A/B Test for Homepage Layout Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Scenario
Designing an A/B test for a new homepage layout
##### Question
Describe how you would design an experiment to measure the impact of the new layout on purchase rate: population, metrics, randomization, sample size, duration, bias control, and analysis plan.
##### Hints
Mention success metric, guardrails, power analysis, segmentation, and validity threats.
Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design an A/B Test for Homepage Layout Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
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:
Population and exposure criteria (inclusions/exclusions)
Metrics
Primary success metric
Secondary/diagnostic metrics
Guardrail metrics
Randomization strategy
Unit of randomization, bucketing, consistency
Sample size and duration
Power analysis and minimum detectable effect (MDE)
Test duration and ramp-up plan
Bias control and validity threats
Key risks and mitigations (e.g., SRM, interference, novelty, seasonality)
Analysis plan
Estimand, statistical tests, confidence intervals
Segmentation/heterogeneity analysis
Multiple testing and decision criteria
Include explicit assumptions as needed. Mention success metric, guardrails, power analysis, segmentation, and validity threats.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?