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 Technical Screen rounds at Yahoo.

What role is this question designed for?

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

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.

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 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?

Quick Overview

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.

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 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

Quick Overview

Evaluate Widget Impact on User Engagement with A/B Testing