Design an A/B test for a search ranking change

Last updated: October 23, 2025

Quick Overview

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

LinkedIn
Analytics & Experimentation
Data Scientist
LinkedIn
October 23, 2025
Data Scientist
Phone Screen
Analytics & Experimentation
Easy

171

5

4,489 solved


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

LinkedIn 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
  • Define clear success metrics aligned with business goals
  • Propose a basic experimental design with control and treatment groups
  • Interpret results correctly and draw reasonable conclusions
  • Identify obvious confounding variables
Key Topics to Cover
Simpson's paradox and ecological fallacy
Long-term vs short-term metrics
Sample size and power calculation
Metric definition and success criteria
Network effects and interference
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
  • How would you handle seasonality in your experiment?
  • How would you handle interference between treatment and control?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
  • What if you discover a bug in the logging during the experiment?
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