How do I approach Analytics & Experimentation interview questions?

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 Onsite 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 for Google Maps UI Change | Google Interview Question

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design A/B Test for Google Maps UI Change states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design A/B Test for Google Maps UI Change

A/B Test Design: Moving the Google Maps Search Bar to the Bottom

Context

Google Maps is considering a UI change: moving the search bar from the top to the bottom of the screen on mobile. We want to evaluate whether this increases search usage without harming user experience or system stability.

Task

Design an experiment that covers:

Hypotheses
Primary success metric(s) and secondary metrics
Guardrail metrics (safety/quality)
Experiment unit and exposure rules
Randomization and bucketing
Sample-size calculation and runtime
Analysis plan and validity checks
Result interpretation and rollout decision

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?

Quick Overview

Task

Design an experiment that covers:

Hypotheses

Primary success metric(s) and secondary metrics

Guardrail metrics (safety/quality)

Experiment unit and exposure rules

Randomization and bucketing

Sample-size calculation and runtime

Analysis plan and validity checks

Result interpretation and rollout decision

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.

State assumptions about instrumentation, randomization, sample size, and data quality.

Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.

A credible analysis or experiment design with clear assumptions and bias checks.

SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.

An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?

How would you handle novelty effects, seasonality, or selection bias?

What decision would you make if metrics disagree?

Design A/B Test for Google Maps UI Change

Quick Overview

Design A/B Test for Google Maps UI Change

Design A/B Test for Google Maps UI Change

A/B Test Design: Moving the Google Maps Search Bar to the Bottom

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Design A/B Test for Google Maps UI Change

Quick Overview

Design A/B Test for Google Maps UI Change

Design A/B Test for Google Maps UI Change

A/B Test Design: Moving the Google Maps Search Bar to the Bottom

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer