Poisson vs Negative Binomial

📰 Medium · Data Science

Learn the difference between Poisson and Negative Binomial distributions for modeling count data, and when to use each

intermediate Published 15 Sept 2026

Action Steps

Understand the assumptions of the Poisson distribution
Identify when the data exhibits overdispersion
Apply the Negative Binomial distribution to model overdispersed count data
Compare the results of both distributions to determine the best fit
Use Python libraries like statsmodels to implement and test these distributions

Who Needs to Know This

Data scientists and analysts can benefit from understanding the differences between these two distributions to choose the most suitable model for their count data

Key Insight

💡 The Negative Binomial distribution is more suitable for modeling count data with overdispersion, whereas the Poisson distribution assumes equal mean and variance