Design an A/B test for a recommendation algorithm
Last updated: December 1, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Microsoft
December 1, 202510
5
2,357 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Analytics questions at Microsoft evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Take-home Project question tests your end-to-end analytical thinking.
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
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 if you discover a bug in the logging during the experiment?
- How would you handle an experiment where the control and treatment groups are different sizes?
- How would you handle interference between treatment and control?
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