The Negative Binomial Model is a statistical method used to analyze count data, particularly when the data exhibit overdispersion, meaning the variance exceeds the mean. It is often applied in fields like epidemiology and economics to model the number of events occurring in a fixed period, such as the number of times a disease occurs or the number of sales. This model is characterized by two parameters: the number of successes and the probability of success in each trial. It can be seen as a generalization of the Poisson distribution, allowing for greater flexibility in modeling data that do not fit the assumptions of simpler models.