Overdispersion

Overdispersion occurs when the variability in a set of count data (like the number of times an event occurs) is greater than what a basic model, such as the Poisson distribution, expects. In simple terms, it means that the data shows more spread or fluctuation than the model predicts, indicating that additional factors or heterogeneity might be influencing the outcomes. Recognizing overdispersion is important because it suggests that a more complex or different statistical approach is needed to accurately analyze and interpret the data.