The term overdispersion refers to the condition when the variance of an observed dependent (response) variable exceeds the nominal variance, given the respective assumed distribution. This condition occurs frequently when fitting generalized linear models to categorical response variables, and the assumed distribution is binomial, multinomial, ordinal multinomial, or Poisson. When overdispersion occurs, the standard errors of the parameter estimates and related statistics (e.g., standard errors of predicted and residual statistics) must be computed taking into account the overdispersion.

For details, see Agresti (1996); see also Generalized Linear/Nonlinear Models.