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Design A/B Test for New Amazon Recommendation Module

Last updated: Mar 29, 2026

Quick Overview

Design A/B Test for New Amazon Recommendation Module evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  • hard
  • Amazon
  • Analytics & Experimentation
  • Data Scientist

Design A/B Test for New Amazon Recommendation Module

Company: Amazon

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

##### Scenario Amazon wants to test a brand-new product recommendation on the home page. ##### Question Design an A/B test to evaluate the new recommendation module. Define test and control, duration and required sample size. What primary metric would you track? How would you justify its business relevance? Define a p-value and explain how it is used to decide whether the experiment is successful. Describe potential sources of bias in this experiment and how you would guard against them. If you can only expose 5 % of users, how would you ensure adequate statistical power? Suppose the treatment lifts click-through-rate but reduces average order value; how would you decide whether to launch? Outline a causal inference approach (e.g., difference-in-differences or propensity matching) you could apply if randomization were impossible. ##### Hints Cover hypothesis formulation, metric hierarchy, variance reduction, sequential testing and guardrail metrics.

Quick Answer: Design A/B Test for New Amazon Recommendation Module evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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|Home/Analytics & Experimentation/Amazon

Design A/B Test for New Amazon Recommendation Module

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Amazon
Aug 4, 2025, 10:55 AM
hardData ScientistOnsiteAnalytics & Experimentation
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0

Design A/B Test for New Amazon Recommendation Module

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:

  1. Hypotheses and experiment design (test vs. control, randomization unit, targeting, and triggering).
  2. Metric hierarchy: primary outcome, secondary metrics, and guardrails (with business justification).
  3. Sample size and duration: how to compute, with a small numeric example; include variance-reduction options.
  4. Statistical testing plan: define the p-value, how it informs decisions, and how to handle sequential looks.
  5. Biases: potential sources and how you would mitigate them.
  6. Limited exposure: if only 5% of users can be exposed, how to ensure adequate power.
  7. Trade-off decision: treatment raises click-through-rate (CTR) but lowers average order value (AOV); how to decide whether to launch.
  8. 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.

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?
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