Explain multiple testing correction with an example
Last updated: December 19, 2025
Quick Overview
Explain multiple testing correction in simple terms and provide a concrete example.
Databricks
December 19, 20252
5
2,261 solved
Explain multiple testing correction in simple terms and provide a concrete example.
This statistics question from Databricks's Technical Screen tests your ability to apply mathematical reasoning to practical problems. The interviewer expects precise definitions, correct methodology, and awareness of assumptions and limitations.
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 design a follow-up experiment based on these results?
- What if the sample size is very small?
- What assumptions does this test make, and how would you validate them?
- How would you explain this result to a non-technical audience?
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Problem Formulation
In this scenario, we need to conduct multiple hypothesis tests simultaneously. Suppose we are testing 10 different drugs to see if they have an effect on reducing blood pressure. Each test has a null ...
Solution Approach
To correct for multiple testing, we can use the Bonferroni correction method. This method adjusts the significance level by dividing the desired alpha level by the number of tests. For our case with 1...
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