> If no random variable exists (such as blocking
> effect) and the data is balanced, I’d present
> arithmetic means with standard deviations obtained
> from PROC GLM with MEANS statement.
I would hope you don't really mean this. If you have no random variable, then you have no statistical analysis. Just about any data you collect in the real world is a random variable.
> But, the dataset I deal with contains a random
> variable as well as occasional imbalance among groups
> as a result of extreme outliers. In such case, I
> prefer to use PROC MIXED with LSMEANS statement.
This is rather unclear as well. Imbalance, in the statistical sense, refers to difference in sample size between groups. Imbalance, in the statistical sense, cannot result because of outliers. So, do you mean you have an unbalanced design (different sample sizes in groups) along with outliers? Or not? Or both?
In any event, the method for dealing with outliers is not LSMEANS. LSMEANS could be used to deal with unbalanced designs (unequal sample sizes in each cell) in two-way or greater designs, but again, LSMEANS offers you no protection against outliers. (In a simple one-way design with unbalanced sample sizes, the means are the same as the LSMEANS)
There are several ways to deal with outliers (regardless of balance or imbalance in the design). One is to use a robust estimation procedure (PROC ROBUSTREG). Another is to fit a model, eliminate the outliers, and then re-fit. I'm sure there are other methods people use as well.