Design an A/B test for a new checkout flow

Last updated: December 3, 2025

Quick Overview

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

HubSpot
Analytics & Experimentation
Product Manager
HubSpot
December 3, 2025
Product Manager
Onsite
Analytics & Experimentation
Hard

18

13

4,035 solved


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

HubSpot asks this during the Onsite to assess your experimentation skills. They want to see how you define success metrics, design controlled experiments, and interpret results with appropriate statistical rigor.

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
Guardrail metrics
Segmentation and heterogeneous effects
Long-term vs short-term metrics
Novelty and primacy effects
Sample size and power calculation
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?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
  • How would you handle interference between treatment and control?
  • How would you handle an experiment where the control and treatment groups are different sizes?
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