Interpret mixed results across segments from an experiment
Last updated: March 26, 2026
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Postmates
March 26, 2026190
5
2,064 solved
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Statistics questions at Postmates 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
- 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
- What if the sample size is very small?
- How would you design a follow-up experiment based on these results?
- What assumptions does this test make, and how would you validate them?
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Browse Statistics QuestionsSample Answer
Problem Formulation
In this experiment, we are evaluating the effectiveness of a new feature or marketing strategy implemented by Postmates. The results show a 3% lift in the desired metric (e.g., order volume, customer ...
Solution Approach
To analyze the results further, we consider the implications of the p-value in relation to the significance level (alpha). Common practice sets alpha at 0.05, which means:
- If p < 0.05, we reject H0...
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