Google·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Got a statistics concept question at Google for a DS role. Just one question but it required a pretty thorough answer covering definition, mechanism, and a worked example.

Questions Asked (1)

What is Simpson's paradox, and how can data imbalance or confounding variables produce it? Walk through a concrete example.

Product Analytics & MetricsA/B Testing & ExperimentationRoot Cause Analysis

Author's notes

I knew the textbook definition but fumbled when they pushed for a real example.

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AI HintsAI Generated

Suggested Approach

Define Simpson's paradox clearly, then explain how data imbalance or confounding variables can cause it by creating a situation where a trend in subgroups reverses when aggregated. Walk through a concrete example, such as a medical treatment or A/B test, showing the reversal and identifying the confounding variable.

Pro tip: Emphasize that Simpson's paradox is not just a statistical curiosity but a critical consideration in A/B testing and product analytics, where ignoring it can lead to wrong business decisions. Mention that at Google, this often arises when analyzing user segments with different baseline behaviors.

1. Define Simpson's Paradox

State that Simpson's paradox occurs when a trend appears in several groups of data but disappears or reverses when the groups are combined.

2. Explain the Role of Confounding Variables

Describe how a confounding variable (e.g., group size or baseline rate) can influence both the grouping and the outcome, creating the paradox.

3. Provide a Concrete Example

Walk through a specific example, such as a drug trial with unequal group sizes or an A/B test with imbalanced segments, showing the reversal.

4. Connect to Data Science Practice

Discuss implications for A/B testing, product metrics, and root cause analysis, emphasizing the need to segment data and control for confounders.

Key Points to Mention

Definition of Simpson's paradox: trend reversal upon aggregation.
Confounding variable: a variable that influences both the independent and dependent variables.
Data imbalance: unequal sample sizes across groups can lead to misleading aggregate results.
Concrete example: e.g., kidney stone treatment success rates (real-world example) or an A/B test with user segments.
Importance in A/B testing: always check for segment-level effects and potential confounders.
Mitigation strategies: stratified analysis, randomization, or causal inference methods.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.