Design an A/B test for a search ranking change

Last updated: August 28, 2025

Quick Overview

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

Discord
Analytics & Experimentation
Data Scientist
Discord
August 28, 2025
Data Scientist
Onsite
Analytics & Experimentation
Medium

184

6

2,799 solved


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

Analytics questions at Discord evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Onsite 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
Novelty and primacy effects
Metric definition and success criteria
Segmentation and heterogeneous effects
A/B testing methodology
Simpson's paradox and ecological fallacy
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 you discover a bug in the logging during the experiment?
  • How would you handle interference between treatment and control?
  • What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
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