Design an Effective A/B Test for Algorithm Launch

Q: Design an Effective A/B Test for Algorithm Launch

This question evaluates a data scientist's competency in experimental design, causal inference, instrumentation, metric design, and statistical analysis for online A/B testing.

Q: 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.

Question

Design an A/B Test for a New Mobile App Recommendation Algorithm

Context

A mobile app team plans to ship a new recommendation algorithm that ranks content in the app. Assume:

We can randomize at the user level with sticky assignment.
Traffic is sufficient to run a 50/50 A/B split after a brief ramp.
The algorithm may change engagement and performance (e.g., latency).

Task

Describe, end-to-end, how you would design and run this A/B test:

Experiment design
- Unit of randomization, assignment, segmentation, and ramp plan.
- Exposure definition and logging/instrumentation.
- Analysis plan and stopping rules.
Metrics and success criteria
- Primary, secondary, and guard-rail metrics.
- How you will define success and launch criteria.
Sample size and test duration
- How to determine required sample size and duration.
- Include formulas and a small numeric example.
Common pitfalls and mitigations
- Discuss randomization, segmentation, statistical power, guard-rail metrics, stopping rules, and launch criteria.

Design an Effective A/B Test for Algorithm Launch

Design an A/B Test for a New Mobile App Recommendation Algorithm

Context

Task

Solution

Comments (0)

Design an Effective A/B Test for Algorithm Launch

Overview

Design an A/B Test for a New Mobile App Recommendation Algorithm

Context

Task

Solution

Comments (0)