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 Onsite rounds at Amazon.

What role is this question designed for?

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

Design A/B Test for New Amazon Recommendation Module

Quick Overview

This question evaluates proficiency in online experimentation, statistical inference, metric design, and causal reasoning for a new home-page recommendation feature, covering hypothesis formulation, metric hierarchy and guardrails, sample-size/duration calculation, variance reduction, sequential testing, bias sources, and alternative causal-inference approaches. It is in the Analytics & Experimentation domain and is commonly asked because product and data teams must quantify the causal impact of UI/algorithm changes and balance engagement versus revenue trade-offs; the prompt tests both conceptual understanding of experimental principles and practical application of statistical and causal techniques.

A/B Test Design: Home Page Recommendation Module

Scenario

Amazon plans to introduce a new product recommendation module on the home page and wants to evaluate its impact via online experimentation.

Task

Design an A/B test that covers:

Hypotheses and experiment design (test vs. control, randomization unit, targeting, and triggering).
Metric hierarchy: primary outcome, secondary metrics, and guardrails (with business justification).
Sample size and duration: how to compute, with a small numeric example; include variance-reduction options.
Statistical testing plan: define the p-value, how it informs decisions, and how to handle sequential looks.
Biases: potential sources and how you would mitigate them.
Limited exposure: if only 5% of users can be exposed, how to ensure adequate power.
Trade-off decision: treatment raises click-through-rate (CTR) but lowers average order value (AOV); how to decide whether to launch.
If randomization is not possible, outline a causal inference approach (e.g., difference-in-differences or propensity matching).

Include hypothesis formulation, metric hierarchy, variance reduction, sequential testing, and guardrail metrics.

Design A/B Test for New Amazon Recommendation Module

Quick Overview

A/B Test Design: Home Page Recommendation Module

Scenario

Task

Solution

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