When you design an experiment, you choose how many experimental units to assign to each combination of levels (or cells) in
the classification. In order to achieve good statistical properties and simplify the computations, you typically attempt to
assign the same number of units to every cell in the design. Such designs are called *balanced designs*.

In SAS/STAT software, you can use the ANOVA procedure to perform analysis of variance for balanced data. The ANOVA procedure
performs computations for analysis of variance that assume the balanced nature of the data. These computations are simpler
and more efficient than the corresponding general computations performed by PROC GLM. Note that PROC ANOVA can be applied
to certain designs that are not balanced in the strict sense of equal numbers of observations for all cells. These additional
designs include all one-way models, regardless of how unbalanced the cell counts are, as well as Latin squares, which do not
have data in all cells. In general, however, the ANOVA procedure is recommended only for balanced data. **If you use ANOVA to analyze a design that is not balanced, you must assume responsibility for the validity of the output.** You are responsible for recognizing incorrect results, which might include negative values reported for the sums of squares.
If you are not certain that your data fit into a balanced design, then you probably need the framework of general linear models
in the GLM procedure.