Question

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.

Answer 1

Overdispersion tends to Limit standard error which creates two problems

1) test statistics of regression parameters are computed by dividing by the standard error so if the standard error is too small then the test statistic will be too big and falsely deemed significant

2) confidence intervals are computed from Standard errors so if the standard error is too small then the confidence interval will be too narrow and make us overconfident about the likely relationship between predictors and the outcome in the population.

In short, overdispersion doesn't affect the model's parameters themselves but biases our conclusions about their significance and the population value.

Hence option A is right