Explain multiple testing correction with an example
Last updated: March 18, 2026
Quick Overview
Explain multiple testing correction in simple terms and provide a concrete example.
Microsoft
March 18, 2026129
5
4,402 solved
Explain multiple testing correction in simple terms and provide a concrete example.
Microsoft values data-driven decision making. This Phone 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
- 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 if the sample size is very small?
- How would you design a follow-up experiment based on these results?
- How would you explain this result to a non-technical audience?
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When conducting multiple hypothesis tests, such as testing the effect of different features on user engagement in a software application, the risk of Type I errors (false positives) increases with the...
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To correct for multiple testing, we commonly use the Bonferroni correction method. This method adjusts the significance level by dividing by the number of tests performed:
[ \alpha_{adj...
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