Design an A/B test for a pricing model
Last updated: August 15, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
PayPal
August 15, 2025137
14
3,415 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
PayPal values data-driven decision making. This Onsite 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 design a follow-up experiment based on these results?
- What alternative statistical method could you use here?
- How would you explain this result to a non-technical audience?
- What assumptions does this test make, and how would you validate them?
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Browse Statistics QuestionsSample Answer
Problem Formulation
To test the impact of new pricing tiers on user engagement and revenue, we will set up an A/B test with two groups: a control group (Group A) using the current pricing model and a treatment group (Gro...
Solution Approach
- Define Hypotheses:
- Null Hypothesis (H0): There is no difference in ARPU between the two groups.
- Alternative Hypothesis (H1): There is a difference in ARPU between the two groups.
...
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