The inverse probability theorem of Bayes is used, along with sampling theory, to obtain objective criteria for choosing among rival models. Formulas are given for the relative posterior probabilities of candidate models and for their goodness of fit, when the models are fitted to a common data set with Normally distributed errors. Cases of full, partial and minimal variance information are treated. The formulas are demonstrated with three examples, including a kinetic study of a catalytic reaction.