Proper family choice of GLM I am debating this with some friends at the moment and would like to know what the stack exchange community can add to this discussion.  When selecting the choice of the distribution and link function, some say that it is less important, even if the outcome variable does not follow the selected distribution. I beg to differ, because this would invalidate all statistical results obtained from the glm. However, it seems that this is quite common to ignore if the distribution just 'seems' to follow a particular distribution.   So, my question is, how important is it that the choice of the distribution is properly chosen? Hence, by making use of statistical test (e.g. ks test).  If anyone could also provide an example as to why this is/is not important I would appreciate it.  Thanks!
Family in GLM - how to choose the right one? When modeling data sampled in the field, I often come across the problem of determining the Family of the dependent variable for GLM (or GLMM). An example: in an ecological study, I have ~ 60 patches.  I have sampled territories of an animal species within these patches as well as a set of explanatory variables.  The number of territories per patch (NTP) is the dependent variable in a GLM.   NTP is integer with values between 0 and 14:      How can I test for the family of NTP?  Any help appreciated as this is an issue many ecologists come across each time they conduct statistical analysis.
Why is chi square used when creating a confidence interval for the variance? This is a very basic question. Why do we use a chi square distribution? What is the meaning of this distribution? Why is this the distribution used for creating a confidence interval for the variance?   Every place I google for an explanation just presents this a fact, explaining when to use chi, but not explaining why to use chi, and why it look the way it does.  Many thanks to anyone that can point me towards the right direction and that is - really understanding why I am using chi when I am creating a confidence interval for the variance.