Design an A/B test for a search ranking change

Last updated: January 8, 2026

Quick Overview

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

Slack
Analytics & Experimentation
Product Manager
Slack
January 8, 2026
Product Manager
Phone Screen
Analytics & Experimentation
Hard

14

7

2,646 solved


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

Analytics questions at Slack 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 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
Simpson's paradox and ecological fallacy
Segmentation and heterogeneous effects
Metric definition and success criteria
A/B testing methodology
Guardrail 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?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
  • How would you handle seasonality in your experiment?
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