Design an A/B test for a recommendation algorithm
Last updated: December 4, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Redfin
December 4, 202530
4
4,809 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
This statistics question from Redfin's Phone Screen tests your ability to apply mathematical reasoning to practical problems. The interviewer expects precise definitions, correct methodology, and awareness of assumptions and limitations.
What the Interviewer Expects
- Set up the problem formally with proper notation
- Apply the correct statistical test with clear justification
- Interpret results with appropriate caveats and confidence levels
- Discuss practical significance vs statistical significance
- Identify potential confounders and how to address them
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
- What alternative statistical method could you use here?
- What if the sample size is very small?
- How would you handle multiple comparisons?
Sharpen Your Skills on Codemia
Practice similar problems with our interactive workspace, get AI feedback, and track your progress.
Browse Statistics QuestionsSample Answer
Problem Formulation
To evaluate the impact of new pricing tiers on user engagement and conversion rates, we will set up an A/B test with two groups: a control group that continues with the existing pricing structure and ...
Solution Approach
-
Sample Size Calculation: We will use the formula for sample size in a two-proportion Z-test:
[ N = \frac{(Z_{\alpha/2} + Z_{\beta})^2 \cdot (p_1(1 - p_1) + p_2(1 - p_2))}{(p_1 - p_2)^2}...
Submit Your Answer
