How do I approach Statistics & Math interview questions?

Statistics & Math questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master statistics & math interviews.

What difficulty level is this interview question?

This is a medium difficulty Statistics & Math 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.

Model Unique Recipients with Poisson Distribution and Test Fit

Q: Model Unique Recipients with Poisson Distribution and Test Fit

This question evaluates understanding of count-data modeling and statistical goodness-of-fit assessment, focusing on distributional assumptions for modeling the number of unique recipients per caller.

Modeling Unique Recipients per Caller

Scenario

You need to model the distribution of the count of unique recipients each caller contacts in a fixed time window. The goal is to use this model for capacity and growth planning (e.g., forecasting server load, messaging throughput, storage).

Question

Which statistical distribution would you use to model the number of recipients per caller, and how would you test the goodness-of-fit?

Guidance

Consider common count distributions: Poisson, Binomial, Negative Binomial (and variants).
Check dispersion (mean vs variance) and the presence of excess zeros.
Fit via maximum likelihood.
Validate with AIC/BIC, Pearson/Deviance goodness-of-fit, chi-square tests for discrete counts, discrete KS or simulation-based checks.

Modeling Unique Recipients per Caller

Scenario

Question

Which statistical distribution would you use to model the number of recipients per caller, and how would you test the goodness-of-fit?

Guidance

Consider common count distributions: Poisson, Binomial, Negative Binomial (and variants).
Check dispersion (mean vs variance) and the presence of excess zeros.
Fit via maximum likelihood.
Validate with AIC/BIC, Pearson/Deviance goodness-of-fit, chi-square tests for discrete counts, discrete KS or simulation-based checks.

Model Unique Recipients with Poisson Distribution and Test Fit

Quick Overview

Modeling Unique Recipients per Caller

Scenario

Question

Guidance

Solution

Submit Your Answer

Model Unique Recipients with Poisson Distribution and Test Fit

Quick Overview

Modeling Unique Recipients per Caller

Scenario

Question

Guidance

Solution

Submit Your Answer