Yahoo: Evaluate Widget Impact on User Engagement with A/B Testing

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 Evaluate Widget Impact on User Engagement with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

A company is preparing to roll out a new in‑app recommendation widget and needs evidence that it improves user engagement.

Task

Design an A/B experiment to evaluate the widget’s impact on daily active users (DAU) and session length.

Address the following:

Experiment design: unit of randomization, eligibility/triggering, assignment, and exposure.
Metrics: choose primary metric(s) and guardrail metrics, and explain why.
Sample size and runtime: how you would determine them (include assumptions and formulas).
Risks: potential biases and implementation pitfalls to address.

Hints: Consider metric sensitivity, power calculations, novelty effects, and logging bias.

Clarifying Questions to Ask Guidance

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 Guidance

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 Guidance

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?

Evaluate Widget Impact on User Engagement with A/B Testing