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 Technical Screen rounds at Uber.

What role is this question designed for?

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

Analyze T2 Results and Recommend Launch Strategy

Quick Overview

Analyze T2 Results and Recommend Launch Strategy 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.

A/B Test Interpretation, Launch Decision, Segmentation, and Multi-Experiment Error Control

Context

You ran two A/B tests on an e-commerce platform:

T1 and T2 are feature variants intended to impact two business metrics:
- Gross Bookings (GB): pre-fee, pre-incentive order value (a growth metric).
- VC: Variable Contribution (margin) per order (i.e., contribution margin after variable costs). Assumption: A decrease in VC is margin-negative. If your org defines VC differently (e.g., as a contra-revenue where a decrease is good), flip the sign logic accordingly.

Observed Results

T1: No statistically significant change in GB or VC.
T2: Statistically significant increase in GB but statistically significant decrease in VC.
- T2 confidence intervals:
  - GB: [+0.1%, +2.3%] ≈ +$0.48 per order
  - VC: [–2.5%, –1.5%] ≈ –$0.20 per order

Tasks

Explain these results to the PM (statistical vs practical significance; growth vs margin trade-offs; plausible mechanisms).
Decide whether to launch T2 using the given CIs and per-order impacts, and justify the decision.
Design a segmentation analysis to identify cohorts where GB lifts without hurting VC.
If you will run 20 parallel feature experiments, define:
- Launch criteria and statistical thresholds for the primary and guardrail metrics.
- How you will control false discoveries and error rates across the portfolio.

Hints

Contrast statistical vs practical significance.
Weigh revenue (GB) vs margin (VC) trade-offs.
Apply multiple-testing corrections where appropriate.
Use principled cohort discovery techniques that avoid p-hacking.

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

Context

You ran two A/B tests on an e-commerce platform:

T1 and T2 are feature variants intended to impact two business metrics:

Gross Bookings (GB): pre-fee, pre-incentive order value (a growth metric).
VC: Variable Contribution (margin) per order (i.e., contribution margin after variable costs). Assumption: A decrease in VC is margin-negative. If your org defines VC differently (e.g., as a contra-revenue where a decrease is good), flip the sign logic accordingly.

Tasks

Explain these results to the PM (statistical vs practical significance; growth vs margin trade-offs; plausible mechanisms).

Decide whether to launch T2 using the given CIs and per-order impacts, and justify the decision.

Design a segmentation analysis to identify cohorts where GB lifts without hurting VC.

If you will run 20 parallel feature experiments, define:

Launch criteria and statistical thresholds for the primary and guardrail metrics.
How you will control false discoveries and error rates across the portfolio.

Hints

Contrast statistical vs practical significance.

Weigh revenue (GB) vs margin (VC) trade-offs.

Apply multiple-testing corrections where appropriate.

Use principled cohort discovery techniques that avoid p-hacking.

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?

Analyze T2 Results and Recommend Launch Strategy

Quick Overview

Analyze T2 Results and Recommend Launch Strategy

A/B Test Interpretation, Launch Decision, Segmentation, and Multi-Experiment Error Control

Context

Observed Results

Tasks

Hints

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer

Analyze T2 Results and Recommend Launch Strategy

Quick Overview

Analyze T2 Results and Recommend Launch Strategy

A/B Test Interpretation, Launch Decision, Segmentation, and Multi-Experiment Error Control

Context

Observed Results

Tasks

Hints

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer