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.

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

Probability distributions

Multiple testing correction (Bonferroni, FDR)

Conditional probability and Bayes theorem

Central Limit Theorem

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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Sample Answer

Setting Up the Problem

Start by formalizing the problem: 1. **Define the hypotheses**: H0 (null hypothesis) and H1 (alternative). Be precise about what you're testing. 2. ...

Solution Approach

**Compute the test statistic**: Apply the appropriate formula using the sample data. **Find the p-value**: The probability of observing a result this...

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