Overdispersion:
A)All of these are correct.
B)Tends to limit standard errors.
C)Doesn't affect the model parameters (b-values) .
D)Biases our conclusions about the significance and population value of the model parameters.

Question

Quizplus · Accepted Answer

Overdispersion refers to the phenomenon where the observed variance in a dataset is larger than would be expected based on the model being used. It does not limit standard errors; rather, it tends to inflate them because the observed data exhibit more variability than the model accounts for. This does not directly affect the model parameters (b-values) themselves but can bias conclusions about the significance and population value of these parameters because standard errors are used to calculate confidence intervals and p-values. Therefore, all of the statements except for B accurately describe aspects of overdispersion.