Design an A/B test for a pricing model
Last updated: November 6, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
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Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Reddit values data-driven decision making. This Technical 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
- 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
- How would you handle multiple comparisons?
- What assumptions does this test make, and how would you validate them?
- What alternative statistical method could you use here?
- What if the sample size is very small?
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Problem Formulation
To design an A/B test for Reddit's new pricing tiers, we need to define our null and alternative hypotheses. Let:
- H0 (Null Hypothesis): There is no difference in the conversion rate of users be...
Solution Approach
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Determine Sample Size: We will use the formula for sample size calculation in A/B testing:
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