A large likelihood ratio? I have two likelihood functions -- at what values of the parameter arguments should I evaluate them when forming the ratio? Given that one of the versions is nested in the other at the boundary of the parameter space (Gaussian errors versus Student-t errors with degrees of freedom fit to the data), what counts as a large enough likelihood ratio to prefer the more general version of the model?