Five univariate divisive clustering methods for grouping means in analysis of variance are considered.Unlike pairwise multiple comparison procedures, cluster analysis has the advantage of producing non-overlapping groups of the treatment means. Comparisonwise Type I error rates and average numbers of clusters per experiment are examined for a heterogeneous set of 20 true treatment means with 11 embedded homogenous sub-groups of one or more treatments. The results of a simulation study clearly show that observed comparisonwise error rate and number of clusters are determined to a far greater extent by the precision of the experiment (as determined by the magnitude of the standard deviation) than by either the stated significance level or the clustering method used.