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

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

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

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.

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

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

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

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

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.

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

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

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

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?

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

Quick Overview

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

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

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

Scenario

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

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

Quick Overview

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

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

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

Scenario

Task

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer