Design an A/B test for a pricing model
Last updated: September 28, 2025
Quick Overview
Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
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Design an experiment to test the impact of a redesigned homepage. Include sample size calculation, metrics, and analysis plan.
Statistics questions at Pinterest test your ability to reason quantitatively and design rigorous experiments. This Onsite 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 test the impact of a redesigned homepage on user engagement and conversion rates, we will set up an A/B test. The null hypothesis (H0) states that there is no difference in engagement metrics betwe...
Solution Approach
- Sample Size Calculation: To determine the necessary sample size for our A/B test, we need to define our expected effect size, significance level (alpha), and power (1 - beta). For this example,...
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