Design an A/B test for a search ranking change

Last updated: March 8, 2026

Quick Overview

Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.

Square/Block
Analytics & Experimentation
Product Manager
Square/Block
March 8, 2026
Product Manager
Phone Screen
Analytics & Experimentation
Hard

203

4

2,016 solved


Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.

Square/Block asks this during the Phone Screen to assess your experimentation skills. They want to see how you define success metrics, design controlled experiments, and interpret results with appropriate statistical rigor.

What the Interviewer Expects
  • Design complex experimentation strategies for tricky scenarios
  • Handle multi-armed bandits, switchback experiments, and quasi-experiments
  • Address long-term effects vs short-term metrics
  • Propose causal inference methods when randomization is not possible
  • Build a measurement framework that connects metrics to business value
  • Discuss organizational experimentation culture and maturity
Key Topics to Cover
Long-term vs short-term metrics
Metric definition and success criteria
Guardrail metrics
A/B testing methodology
Novelty and primacy effects
How to Approach This
  1. Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
  2. Run experiments long enough to account for novelty effects and weekly seasonality.
  3. Use funnel analysis to identify where users drop off for maximum optimization impact.
  4. Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
  5. Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
  • What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • How would you handle interference between treatment and control?
  • What if you discover a bug in the logging during the experiment?
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