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 TikTok.

What role is this question designed for?

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

Design A/B Test for Cost-Per-Conversion Efficiency Analysis

Quick Overview

This question evaluates a data scientist's competency in experimental design and metrics engineering for multi-arm A/B tests, including defining cost-per-conversion metrics, randomization and contamination controls, sample size and power calculations, statistical hypothesis testing, and post-hoc heterogeneity analyses.

Multi-Arm A/B Test: Comparing Cost-Per-Conversion Across Channels

Scenario

You need to compare four new acquisition channels—YouTube ads, Google Search ads, Facebook ads, and Direct Mail—to choose the most cost-efficient option for driving conversions given a fixed budget.

Task

Design a rigorous multi-arm A/B test to evaluate cost-per-conversion efficiency across these channels.

Address the following:

Primary metric
- What exactly will you optimize? Define the metric precisely (including incrementality vs. attribution, unit of analysis, and conversion window).
Experimental design
- Randomization scheme (units, arms, control), avoiding cross-channel contamination, frequency caps, and deduped conversions.
Hypotheses and statistical test
- State the null and alternative hypotheses, and specify the appropriate global and pairwise tests.
Sample size, budget split, and duration
- How will you determine these given desired power and minimum detectable effect (MDE)? Include how per-user costs differ by channel.
Post-hoc / follow-up analyses
- What analyses will you run after the main test (e.g., multiple comparisons, heterogeneity, creative, response curves)?

Hints: Discuss cost-per-conversion metric, multi-arm design, power/alpha, ANOVA vs. pairwise tests, budget allocation, assumptions, and demographic/creative differences.

Quick Overview

Task

Design a rigorous multi-arm A/B test to evaluate cost-per-conversion efficiency across these channels.

Address the following:

Primary metric

What exactly will you optimize? Define the metric precisely (including incrementality vs. attribution, unit of analysis, and conversion window).

Experimental design

Randomization scheme (units, arms, control), avoiding cross-channel contamination, frequency caps, and deduped conversions.

Hypotheses and statistical test

State the null and alternative hypotheses, and specify the appropriate global and pairwise tests.

Sample size, budget split, and duration

How will you determine these given desired power and minimum detectable effect (MDE)? Include how per-user costs differ by channel.

Post-hoc / follow-up analyses

What analyses will you run after the main test (e.g., multiple comparisons, heterogeneity, creative, response curves)?

Hints: Discuss cost-per-conversion metric, multi-arm design, power/alpha, ANOVA vs. pairwise tests, budget allocation, assumptions, and demographic/creative differences.

Design A/B Test for Cost-Per-Conversion Efficiency Analysis

Quick Overview

Multi-Arm A/B Test: Comparing Cost-Per-Conversion Across Channels

Scenario

Task

Solution

Comments (0)

Design A/B Test for Cost-Per-Conversion Efficiency Analysis

Quick Overview

Multi-Arm A/B Test: Comparing Cost-Per-Conversion Across Channels

Scenario

Task

Solution

Comments (0)