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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

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This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Evaluate College Impact on Income: Address Bias and Validity | Google Interview Question

Evaluate College Impact on Income: Address Bias and Validity

Last updated: Mar 29, 2026

Quick Overview

This question evaluates competency in critiquing observational study design and analysis, including recognition of sampling bias and external validity issues, regression model assumptions, confounding, diagnostic reasoning, and the distinction between descriptive and causal inference.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Analytics & Experimentation

Study Setup

You have an observational, cross-sectional dataset of 1,000 adult Mountain View residents. The outcome is individual annual income (pre-tax). The exposure is a binary indicator of having completed a 4-year college degree (College = 1 if yes, 0 otherwise).

Question

Is fitting a simple linear regression of income on a binary college indicator appropriate for this data? Discuss:

Sampling bias and external validity.
Model validity and assumptions (distributional form, confounding, diagnostics).
Better analytic alternatives for both descriptive and causal goals.

Solution

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