Interpret mixed results across segments from an experiment
Last updated: September 10, 2025
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Discord
September 10, 20257
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An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Discord values data-driven decision making. This Take-home Project 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
- What alternative statistical method could you use here?
- What assumptions does this test make, and how would you validate them?
- How would you explain this result to a non-technical audience?
- How would you design a follow-up experiment based on these results?
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Browse Statistics QuestionsSample Answer
Problem Formulation
In this experiment, we are interested in determining whether a 3% lift in a metric (e.g., user engagement) is statistically significant. The null hypothesis (H0) states that there is no effect (the li...
Solution Approach
- Hypothesis Testing: We compare the p-value to a significance level (alpha). A common alpha is 0.05. Since 0.08 > 0.05, we fail to reject the null hypothesis, suggesting that the evidence for a ...
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