Design an A/B test for a new checkout flow

Last updated: August 20, 2025

Quick Overview

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

Supabase
Analytics & Experimentation
Data Scientist
Supabase
August 20, 2025
Data Scientist
Onsite
Analytics & Experimentation
Easy

127

0

3,755 solved


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

This analytics question from Supabase's Onsite 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
Novelty and primacy effects
Funnel analysis and cohort analysis
Segmentation and heterogeneous effects
A/B testing methodology
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?
  • How would you handle seasonality in your 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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