Design an A/B test for a recommendation algorithm
Last updated: October 8, 2025
Quick Overview
Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
Zscaler
October 8, 202512
4
2,173 solved
Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
Zscaler 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 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 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?
- What if you discover a bug in the logging during the experiment?
- How would you handle seasonality in your experiment?
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Browse Analytics QuestionsSample Answer
Problem Setup
To evaluate the impact of a redesigned homepage on user engagement, we need to define a clear analytical question: **Does the new homepage design lead to a statistically significant increase in user e...
Methodology
To determine the appropriate sample size, we will use the following parameters:
- Desired significance level (alpha) = 0.05
- Power (1 - beta) = 0.8
- Minimum detectable effect size (e.g., 5% increase...
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