e.s.e.

stands for effective standard error – the adjective effective denotes that it is appropriate only for comparisons within a table of effects that are unaffected by the constraints within the table. For example, the effects in a one-way table will be constrained by the fact that a grand mean will have been fitted beforehand. So their sum (weighted by their replications if unequal) will be zero. So the e.s.e. is appropriate for obtaining an s.e.d. to assess differences between effects, but not for testing the sum of the effects, nor any individual effect, against zero.