In this section, we look at a number of complications and concerns that arise in the analysis of tied data using maximum likelihood methods.
A common reaction to the methods described in this chapter is that there must be something wrong. In general, when multiple observations are created for a single individual, it’s reasonable to suppose that those observations are not independent, thereby violating a basic assumption used to construct the likelihood function. The consequence is thought to be biased standard error estimates and inflated test statistics. Even worse, there are different numbers of observations for different individuals, so some appear to get more weight than others.