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 medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Meta.

What role is this question designed for?

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

Convince Product Manager to Launch 'Show Similar Products' Button

Quick Overview

This question evaluates a data scientist's skills in experimental design, causal inference, metric selection, and historical modeling using observational interaction logs, and falls under the Analytics & Experimentation domain for a Data Scientist role.

Scenario

Instagram is considering adding a "Show similar products" button on product-tagged content to boost shopping-related engagement. The feature has not launched yet.

Task

Convince a Product Manager the feature is worth testing and design an experiment, using only existing interaction data (no feature data yet), to estimate potential impact and make a launch decision.

Questions

What proxy metric(s) from existing logs would you use to estimate the feature’s likely impact on engagement, and why do they map to the proposed button?
How would you construct historical baselines and simple models to project impact (upper/lower bounds)?
How would you design an experiment to decide whether to launch the button, including:
- Randomization unit and any cluster randomization to mitigate network effects
- Control versus treatment definition and exposure eligibility
- Primary metrics, secondary metrics, and guardrail (health) metrics
- Significance level, power, and duration
What specific criteria would signal it is safe to roll the feature out broadly?

Quick Overview

Questions

What proxy metric(s) from existing logs would you use to estimate the feature’s likely impact on engagement, and why do they map to the proposed button?

How would you construct historical baselines and simple models to project impact (upper/lower bounds)?

How would you design an experiment to decide whether to launch the button, including:

Randomization unit and any cluster randomization to mitigate network effects
Control versus treatment definition and exposure eligibility
Primary metrics, secondary metrics, and guardrail (health) metrics
Significance level, power, and duration

What specific criteria would signal it is safe to roll the feature out broadly?

Convince Product Manager to Launch 'Show Similar Products' Button

Quick Overview

Scenario

Task

Questions

Solution

Submit Your Answer to Earn 20XP

Convince Product Manager to Launch 'Show Similar Products' Button

Quick Overview

Scenario

Task

Questions

Solution

Submit Your Answer to Earn 20XP