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QT est is specifically designed to carry out the appropriate order-constrained maximum-likelihood estimation and goodness-of-fit tests for virtually all of the models we discuss. 7 Another unusual, and possibly confusing, feature of these models is that they can allow for a perfect fit where, on certain sets of data, a model cannot be rejected no matter how large the significance level.

This is because many of these models do not make point predictions. Rather they make predictions that occupy a volume in the unit cube of binary choice probabilities. When a point representing a set of choice proportions (data) is inside such a model, then the best-fitting choice probabilities are literally equal to the observed choice proportions, hence giving a perfect fit.