Design an A/B test for a recommendation algorithm
Last updated: March 28, 2026
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Meta
March 28, 2026222
5
4,947 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Meta values data-driven decision making. This Phone Screen 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
- State the correct formula or theorem with clear definitions
- Apply the concept to the given scenario step by step
- Interpret the result in plain language
- Identify assumptions and when they might be violated
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 handle multiple comparisons?
- How would you explain this result to a non-technical audience?
- What alternative statistical method could you use here?
- What assumptions does this test make, and how would you validate them?
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Browse Statistics QuestionsSample Answer
Problem Formulation
To evaluate the impact of new pricing tiers on user engagement and conversion rates through an A/B test, we will define the following variables:
- Control Group (A): Users under the current prici...
Solution Approach
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Sample Size Calculation: To determine the sample size needed for this A/B test, we will use the formula for comparing two proportions:
[ n = \frac{(Z_{\alpha/2} + Z_{\beta})^2 \cdot (p_...
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