MSG, the mean square for groups, measures how different the individual means are from the overall mean (~ weighted average of square distances of sample averages to the overall mean). SSG is the sum of squares for groups.

MSE, the mean square for error is the pooled sample variance sp2 and estimates the common variance σ2 of the I populations (~ weighted average of the variances from each of the I samples). SSE is the sum of squares for error.