For many designs special attention needs to be given to least square means and contrasts since their standard errors are not necessarily correct. This is true, for example, for split-plot designs. Designed for mixed effects models. Random terms are incorporated into inference from the outset. Contrasts, least square means and estimates of linear combinations are reported with correct standard errors.
Attention needs to be paid to the selection of an appropriate inference space.
If analysis is driven by accounting for degrees of freedoms and tests, p-values, contrasts, least square means etc. are taken for granted. Depending on the design, some results reported by PROC GLM are actually incorrect. While least square means are correct, their standard errors are not necessarily correct. The standard error estimates for least square means account for the random effects. Since it is not based on expected mean squares, there are no sums of squares in the standard output.