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Design A/B Test for Google Maps UI Change

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in experimental design and product analytics, including hypothesis formulation, metric selection, guardrail definition, randomization, sample-size calculation, and analysis planning for a mobile mapping application's UI change.

  • medium
  • Google
  • Analytics & Experimentation
  • Data Scientist

Design A/B Test for Google Maps UI Change

Company: Google

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario The product team wants to move the Google Maps search bar from the top to the bottom of the screen. ##### Question Design an A/B test to evaluate this UI change. Define hypothesis, primary and guardrail metrics, experiment unit, randomization, sample-size calculation, runtime, and how you would interpret the results. ##### Hints Explain success metrics (e.g., searches per session), guardrails (retention, errors), power analysis, novelty effects, and rollout decisions.

Quick Answer: This question evaluates a data scientist's competency in experimental design and product analytics, including hypothesis formulation, metric selection, guardrail definition, randomization, sample-size calculation, and analysis planning for a mobile mapping application's UI change.

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Google logo
Google
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
4
0

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:

  1. Hypotheses
  2. Primary success metric(s) and secondary metrics
  3. Guardrail metrics (safety/quality)
  4. Experiment unit and exposure rules
  5. Randomization and bucketing
  6. Sample-size calculation and runtime
  7. Analysis plan and validity checks
  8. Result interpretation and rollout decision

Solution

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