Design an A/B test for a pricing model
Last updated: October 10, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
LinkedIn
Statistics & Math
Data Scientist
Data Scientist
Take-home Project
Statistics & Math
Easy
3
4
1,359 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Statistics questions at LinkedIn test your ability to reason quantitatively and design rigorous experiments. This Take-home Project question evaluates your understanding of statistical inference and its application to business decisions.
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
Bayesian vs frequentist inference
Confidence intervals and significance levels
Causal inference basics
Hypothesis testing (H0, H1, p-values)
Probability distributions
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 if the sample size is very small?
- What alternative statistical method could you use here?
- How would you handle multiple comparisons?
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Browse Statistics QuestionsSample Answer
Problem Formulation
To design an A/B test for LinkedIn's new pricing model, we need to define our null and alternative hypotheses:
- Null Hypothesis (H0): There is no difference in user engagement (measured by the n...
Solution Approach
- Define Parameters: Assume the current conversion rate (p1) is 5%. We aim to detect a difference of 2% (to p2 = 7%).
- Set Significance Level and Power: Choose α = 0.05 (5%) and power = 0.8...
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