The article compares quasi-Poisson and negative binomial regression models for overdispersed count data. Both models have the same number of parameters and are used to analyze count data where the variance exceeds the mean. While they often yield similar results, they can produce strikingly different estimates of covariate effects. The variance in quasi-Poisson models is linearly related to the mean, whereas in negative binomial models, it is quadratic. These differences affect the weighting in the iteratively weighted least-squares algorithm used to fit the models. Quasi-Poisson and negative binomial regression models weight observations differently based on their mean values. An example using harbor seal counts from aerial surveys shows that the choice of model significantly impacts abundance estimates. The quasi-Poisson model gives more weight to larger sites, while the negative binomial model gives more weight to smaller sites. This difference in weighting leads to different estimates of harbor seal abundance. The article concludes that understanding the theoretical differences between the two models is crucial for ecologists to choose the appropriate method based on their scientific goals.The article compares quasi-Poisson and negative binomial regression models for overdispersed count data. Both models have the same number of parameters and are used to analyze count data where the variance exceeds the mean. While they often yield similar results, they can produce strikingly different estimates of covariate effects. The variance in quasi-Poisson models is linearly related to the mean, whereas in negative binomial models, it is quadratic. These differences affect the weighting in the iteratively weighted least-squares algorithm used to fit the models. Quasi-Poisson and negative binomial regression models weight observations differently based on their mean values. An example using harbor seal counts from aerial surveys shows that the choice of model significantly impacts abundance estimates. The quasi-Poisson model gives more weight to larger sites, while the negative binomial model gives more weight to smaller sites. This difference in weighting leads to different estimates of harbor seal abundance. The article concludes that understanding the theoretical differences between the two models is crucial for ecologists to choose the appropriate method based on their scientific goals.