Design an A/B test for a new checkout flow

Last updated: September 15, 2025

Quick Overview

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

Mastercard
Analytics & Experimentation
Data Scientist
Mastercard
September 15, 2025
Data Scientist
Take-home Project
Analytics & Experimentation
Medium

20

5

4,365 solved


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

This analytics question from Mastercard's Take-home Project 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
  • 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
Guardrail metrics
Network effects and interference
Segmentation and heterogeneous effects
A/B testing methodology
Metric definition and success criteria
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
  • What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
  • How would you handle an experiment where the control and treatment groups are different sizes?
  • How would you handle seasonality in your experiment?
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