Design an A/B test for a recommendation algorithm
Last updated: March 18, 2026
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Lyft
March 18, 202622
6
398 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Statistics questions at Lyft test your ability to reason quantitatively and design rigorous experiments. This Take-home Project question evaluates your understanding of statistical inference and its application to business decisions.
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
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
- How would you design a follow-up experiment based on these results?
- What alternative statistical method could you use here?
- What if the sample size is very small?
- How would you explain this result to a non-technical audience?
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Browse Statistics QuestionsSample Answer
Problem Formulation
To design an A/B test for a new pricing tier algorithm at Lyft, we need to establish a clear hypothesis. Let's define our primary hypothesis as follows:
H0 (Null Hypothesis): The new pricing tier...
Solution Approach
To calculate the sample size needed for the A/B test, we can use the formula for comparing two means:
- Determine the effect size (d): Based on historical data, let’s assume we estimate that the...
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