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 hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Chime.

What role is this question designed for?

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

Design and Analyze A/B Test for Recommendation Widget

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 and Analyze A/B Test for Recommendation Widget states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

You are designing and analyzing an online A/B test for launching a new recommendation widget in a consumer-facing product (e.g., mobile and web app). The widget recommends relevant actions or products on a home/feed surface.

Task

Explain, end-to-end, how you would set up, run, and analyze an A/B test for this recommendation widget.

Requirements

Experiment design and randomization
- Define hypothesis, unit of assignment, eligibility/exposure, and rollout plan.
Sample size and power
- Determine minimum detectable effect (MDE), sample size, duration, and traffic ramp.
Metrics
- Choose a single primary metric, key secondary metrics, and guardrail metrics; define how each is computed.
Execution and debugging
- Instrumentation, logging, pre-checks (e.g., SRM), and live monitoring.
Analysis
- Statistical tests, variance reduction, handling triggered exposure vs ITT, and multiple comparisons.
Pitfalls
- List issues that could invalidate the experiment and how you would detect/mitigate them.
Communication
- How you would communicate results and a go/no-go recommendation to stakeholders.

Assume users can be exposed multiple times across sessions and platforms, and there is no cross-user network effect.

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

Requirements

Experiment design and randomization

Define hypothesis, unit of assignment, eligibility/exposure, and rollout plan.

Sample size and power

Determine minimum detectable effect (MDE), sample size, duration, and traffic ramp.

Metrics

Choose a single primary metric, key secondary metrics, and guardrail metrics; define how each is computed.

Execution and debugging

Instrumentation, logging, pre-checks (e.g., SRM), and live monitoring.

Analysis

Statistical tests, variance reduction, handling triggered exposure vs ITT, and multiple comparisons.

Pitfalls

List issues that could invalidate the experiment and how you would detect/mitigate them.

Communication

How you would communicate results and a go/no-go recommendation to stakeholders.

Assume users can be exposed multiple times across sessions and platforms, and there is no cross-user network effect.

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 and Analyze A/B Test for Recommendation Widget

Quick Overview

Design and Analyze A/B Test for Recommendation Widget