Viewing the analysis of designed experiments as a model selection problem, we introduce the use of a predictive Bayesian criterion in this context based on the predictive density of a replicate experiment (PDRE). A calibration of the criterion is provided to assist in the model choice. The relationships of the proposed criterion to other prevalent criteria, such as AIC, BIC, and Mallows'sCp, are given. An information theoretic criterion based on the PDRE's of two competing models is also introduced and compared with the usualFstatistic for two nested models. Examples are given to illustrate the proposed methodology.