Design an A/B test for a recommendation algorithm

Last updated: May 4, 2026

Quick Overview

Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.

Zoom
Analytics & Experimentation
Product Manager
Zoom
May 4, 2026
Product Manager
Phone Screen
Analytics & Experimentation
Medium

71

6

3,060 solved


Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.

Analytics questions at Zoom evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Phone Screen question tests your end-to-end analytical thinking.

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
Sample size and power calculation
Metric definition and success criteria
A/B testing methodology
Simpson's paradox and ecological fallacy
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 interference between treatment and control?
  • How would you handle seasonality in your experiment?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
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