Statistical methods in model discrimination

Particularly in the chemical kinetic field but also in others in chemistry and engineering there has been much recent attention on the use of statistics in discriminating between rival models. There are two related basic problems. The first is to design experiments which will be most informative in determining which of several possible mathematical models is the “correct” one. Simple illustrations are given of the solution of this problem by different methods, such as the criteria of Roth, Box and Hill and of others, including some work done by R. S. Hawkins and the author on expected entropy change. The second problem is to analyze the data when received. A sampling of likelihood and Bayesian approaches to this problem is described with examples.