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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.

What difficulty level is this interview question?

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.

Design A/B Test to Isolate Product Usage Drop Causes

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in causal inference, experimentation design, and product analytics, focusing on identifying confounders, designing A/B tests, selecting control variables, and communicating causal lift estimates.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Analytics & Experimentation

Investigating a 10–11% Product Usage Drop via Experimentation

Context

You observe that product usage fell by 10% in the U.S. and 11% in Mexico over the same recent period (e.g., last 1–2 weeks vs. prior baseline). No planned outages were announced. You are tasked with diagnosing causes and proposing an experiment to isolate them.

Tasks

Identify plausible confounders that could explain simultaneous decline across both markets.
Design an A/B test (or tests) to isolate the causal driver(s).
Specify variables you would control for in the analysis and setup.
Explain how you would report findings, including causal lift estimates, to stakeholders.

Solution

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