Design an A/B test for a recommendation algorithm

Last updated: August 23, 2025

Quick Overview

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

Oracle
Analytics & Experimentation
Product Manager
Oracle
August 23, 2025
Product Manager
Onsite
Analytics & Experimentation
Medium

4

5

4,130 solved


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

Oracle asks this during the Onsite 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 a rigorous experiment with proper randomization and sample size calculation
  • Define primary and guardrail metrics with clear rationale
  • Address novelty effects, network effects, and interference
  • Segment results appropriately and identify heterogeneous treatment effects
  • Propose follow-up analyses when results are ambiguous
Key Topics to Cover
Novelty and primacy effects
Funnel analysis and cohort analysis
A/B testing methodology
Sample size and power calculation
Long-term vs short-term metrics
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 an experiment where the control and treatment groups are different sizes?
  • What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
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