Overdispersion occurs when the observed variance in a dataset is greater than what would be expected under a given statistical model, typically the Poisson distribution. In toxicological studies, data often involve counts of events, such as the number of adverse effects observed in a group of animals or the number of carcinogenic outcomes in a population. The Poisson distribution assumes that the mean and variance of the data are equal, but real-world data frequently violate this assumption, leading to overdispersion.
