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 medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Walmart Labs.

What role is this question designed for?

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

Calculate Sample Size for Effective A/B Test Design

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 Calculate Sample Size for Effective A/B Test Design states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Calculate Sample Size for Effective A/B Test Design

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

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

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?

Quick Overview

Questions

How do you calculate the required sample size for an A/B test?

What techniques can improve the statistical power of the experiment?

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?

Calculate Sample Size for Effective A/B Test Design

Quick Overview

Calculate Sample Size for Effective A/B Test Design

Calculate Sample Size for Effective A/B Test Design

A/B Test Planning: Sample Size and Power

Scenario

Questions

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer

Calculate Sample Size for Effective A/B Test Design

Quick Overview

Calculate Sample Size for Effective A/B Test Design

Calculate Sample Size for Effective A/B Test Design

A/B Test Planning: Sample Size and Power

Scenario

Questions

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer