Explain Simpson's paradox with an example
Last updated: May 15, 2026
Quick Overview
Explain Simpson's paradox in simple terms and provide a concrete example.
Workday
Statistics & Math
Data Scientist
Workday
May 15, 2026Data Scientist
Take-home Project
Statistics & Math
Easy
92
4
1,198 solved
Explain Simpson's paradox in simple terms and provide a concrete example.
Statistics questions at Workday 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
Power analysis and sample size calculation
Confidence intervals and significance levels
Multiple testing correction (Bonferroni, FDR)
Central Limit Theorem
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 explain this result to a non-technical audience?
- How would you handle multiple comparisons?
- 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
Simpson's paradox occurs when a trend that appears in several different groups of data disappears or reverses when these groups are combined. To illustrate this, let's consider a hypothetical scenario...
Solution Approach
- Calculate Success Rates for Each Department:
- For Department A:
- Program X Success Rate = 80/100 = 0.80 (80%)
- Program Y Success Rate = 20/100 = 0.20 (20%)
- For Department B...
- For Department A:
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