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Calculate Sample Size for Effective A/B Test Design

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in experiment design, sample size calculation, and statistical power analysis for binary conversion metrics within the Analytics & Experimentation domain.

  • medium
  • Walmart Labs
  • Analytics & Experimentation
  • Data Scientist

Calculate Sample Size for Effective A/B Test Design

Company: Walmart Labs

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Designing an A/B test for a marketing feature rollout. ##### Question How do you calculate the required sample size for an A/B test? What techniques can improve the statistical power of the experiment? ##### Hints Use power analysis (effect size, alpha, beta); improve power with variance reduction, stratification, larger samples, longer tests.

Quick Answer: This question evaluates a candidate's competency in experiment design, sample size calculation, and statistical power analysis for binary conversion metrics within the Analytics & Experimentation domain.

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A/B Test Planning: Sample Size and Power

Scenario

You are planning an online A/B test to evaluate a new marketing feature. The primary success metric is a user-level conversion rate (binary: converted vs. not). You need to determine the required sample size and ways to maximize the test's statistical power under realistic constraints.

Questions

  1. How do you calculate the required sample size for an A/B test?
  2. What techniques can improve the statistical power of the experiment?

Solution

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