In cancer research, data often exhibit overdispersion due to the complex and heterogeneous nature of the disease. For example, the number of mutations in tumor samples, the count of immune cells infiltrating different tumor regions, or the number of adverse events experienced by patients undergoing treatment can all vary significantly. The negative binomial distribution provides a better fit for this type of data, allowing for more accurate statistical inference and decision-making.
