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 Meta.

What role is this question designed for?

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

Analyze Change in App Metrics and Feature Impact

Quick Overview

This question evaluates a data scientist's competency in product analytics, experimentation design, causal inference, metric selection, hypothesis framing across the AARRR funnel, and quantitative impact sizing within the Analytics & Experimentation domain.

Scenario

A consumer app has either launched a new feature or observed a sudden change in a key metric. You are asked to investigate and drive a decision.

Tasks

Frame the problem and outline how you would understand what changed, why it changed, and why the business should care.
Metrics
- Propose north-star, secondary, and guard-rail metrics.
- Specify the granularity you would track and why.
Sizing
- Size the magnitude of the impact or opportunity. State assumptions and show calculations.
Hypothesis
- Formulate hypotheses along the user journey (AARRR: Acquisition, Activation, Retention, Revenue, Referral).
- Specify what data and analyses you would use to validate them.
Experiment design
- Design an A/B test: randomization unit, key metrics, network-effect concerns, sanity checks, sample size calculation, and launch decision criteria.
If an A/B test is infeasible
- Outline suitable quasi-experimental approaches and how you would validate assumptions.

Hint: Think top-down: frame problem → pick metrics → state hypothesis → design experiment or analysis → weigh trade-offs.

Quick Overview

Tasks

Frame the problem and outline how you would understand what changed, why it changed, and why the business should care.

Metrics

Propose north-star, secondary, and guard-rail metrics.
Specify the granularity you would track and why.

Sizing

Size the magnitude of the impact or opportunity. State assumptions and show calculations.

Hypothesis

Formulate hypotheses along the user journey (AARRR: Acquisition, Activation, Retention, Revenue, Referral).
Specify what data and analyses you would use to validate them.

Experiment design

Design an A/B test: randomization unit, key metrics, network-effect concerns, sanity checks, sample size calculation, and launch decision criteria.

If an A/B test is infeasible

Outline suitable quasi-experimental approaches and how you would validate assumptions.

Hint: Think top-down: frame problem → pick metrics → state hypothesis → design experiment or analysis → weigh trade-offs.

Analyze Change in App Metrics and Feature Impact

Quick Overview

Scenario

Tasks

Solution

Submit Your Answer to Earn 20XP

Analyze Change in App Metrics and Feature Impact

Quick Overview

Scenario

Tasks

Solution

Submit Your Answer to Earn 20XP