Design an A/B test for a recommendation algorithm
Last updated: February 8, 2026
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Optiver
February 8, 202636
4
4,129 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
This analytics question from Optiver'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
- 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
How to Approach This
- Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
- Run experiments long enough to account for novelty effects and weekly seasonality.
- Use funnel analysis to identify where users drop off for maximum optimization impact.
- Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
- Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
- 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?
- How would you handle interference between treatment and control?
- What if you discover a bug in the logging during the experiment?
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