Goodness of fit tests (GOFTs) validate the closeness of the theoretical distribution
function to the empirical distribution function. They are also known as
empirical distribution function tests. These tests determine how well the distribution
under study fits to a data set. They are used to test simple hypothesis
which completely specifies the model and also the composite hypotheses where
only the name of the model/distribution is stated but not its parameters. In the
latter case, the parameters are estimated from the data. The common GOFTs
are Kolmogorov–Smirnov, Cram´er–von Mises and Anderson–Darling.