Design an A/B test for a new checkout flow

Last updated: July 16, 2025

Quick Overview

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

PayPal
Analytics & Experimentation
Product Manager
PayPal
July 16, 2025
Product Manager
Phone Screen
Analytics & Experimentation
Easy

0

4

2,660 solved


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

This analytics question from PayPal's Phone Screen tests your ability to think critically about data. The interviewer expects you to consider confounding variables, selection bias, and the difference between correlation and causation.

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
Guardrail metrics
Funnel analysis and cohort analysis
Sample size and power calculation
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 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?
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