Two-Way Crossed Classification Without Interaction

Abstract

The major advantage of the one-way classification (one-factor design) discussed in the preceding chapter is its simplicity, which extends to the experimental layout, the model and assumptions underlying the analysis of variance, and the computations involved in the analysis. The major disadvantage of such a design is its relative inefficiency. The error variance will usually be large compared to that resulting from other designs. This is in part offset by the fact that no other design yields as many degrees of freedom for the error variance as does this design.