Design an A/B test for a recommendation algorithm
Last updated: August 24, 2025
Quick Overview
Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
Waymo
Statistics & Math
Data Scientist
Waymo
August 24, 2025Data Scientist
Onsite
Statistics & Math
Hard
112
9
1,883 solved
Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
Waymo values data-driven decision making. This Onsite question assesses whether you can design experiments, interpret results correctly, and avoid common statistical pitfalls like p-hacking or Simpson's paradox.
What the Interviewer Expects
- Derive results from first principles when needed
- Handle complex scenarios with multiple interacting variables
- Design experiments that account for real-world complications
- Discuss advanced topics: Bayesian methods, causal inference, resampling
- Connect statistical concepts to business decision-making
- Identify subtle errors in reasoning (Simpson's paradox, survivorship bias)
Key Topics to Cover
Regression analysis
Causal inference basics
Hypothesis testing (H0, H1, p-values)
Multiple testing correction (Bonferroni, FDR)
Central Limit Theorem
Confidence intervals and significance levels
How to Approach This
- Define your hypotheses (H0 and H1) clearly before performing any test.
- Calculate required sample size BEFORE running an experiment, using power analysis.
- Remember the Central Limit Theorem: sample means become approximately normal with large n.
- Watch for Simpson's paradox. Always segment data by key dimensions.
- Distinguish between statistical significance and practical significance.
Possible Follow-up Questions
- What assumptions does this test make, and how would you validate them?
- How would you design a follow-up experiment based on these results?
- How would you handle multiple comparisons?
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Browse Statistics QuestionsSample Answer
Problem Formulation
To evaluate the impact of a redesigned homepage on user engagement, we will set up an A/B test framework. Let:
- A: the control group, which sees the original homepage.
- B: the treatment grou...
Solution Approach
- Sample Size Calculation: We need to determine the sample size required to detect a statistically significant difference in CTR. We will use the formula for sample size in A/B testing: [ n =...
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