An alternative analysis of the traditional two-group single response variable design is discussed. The proposed analysis involves the classification, or assignment, of experimental units to populations represented by the two groups. Methods of estimating probabilities of correct classification are discussed. Three real data sets are provided to illustrate the utility of a classification analysis in describing both group as well as individual differences. A table of sample sizes required to yield estimates of probabilities of correct classification within a given tolerance of optimum probabilities is presented. Operational definitions of "substantive significance" are proposed.
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