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 question evaluates a candidate's skills in experimental design, randomization and exposure logic, sample-size and power calculations, metric selection and causal/statistical analysis, instrumentation and monitoring, and stakeholder communication for product experiments.

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.

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.

Design and Analyze A/B Test for Recommendation Widget

Quick Overview

Scenario

Task

Requirements

Solution

Comments (0)

Design and Analyze A/B Test for Recommendation Widget

Quick Overview

Scenario

Task

Requirements

Solution

Comments (0)