It is very often used on its own, and in fact quite frequently produces reasonable trees. However, it lacks any sort of tree search and optimality criterion, and so there is no guarantee that the recovered tree is the one that best fits the data. A more appropriate analytical procedure would be to use NJ to produce a starting tree, then employ a tree search using an optimality criterion, to ensure that the best tree is recovered. Many scientists eschew distance methods, for various reasons. A commonly cited reason is that distances are inherently phenetic rather than phylogenetic, in that they do not distinguish between ancestral similarity (symplesiomorphy) and derived similarity (synapomorphy). This criticism is not entirely fair: most currently implementations of parsimony, likelihood, and Bayesian phylogenetic inference use time-reversible character models, and thus accord no special status to derived or ancestral character states. Under these models, the tree is estimated unrooted; rooting, and consequently determination of polarity, is performed after the analysis. The primary difference between these methods and distances is that parsimony, likelihood, and Bayesian methods fit individual characters to the tree, whereas distance methods fit all the characters at once. There is nothing inherently less phylogenetic about this approach. More practically, distance methods are avoided because the relationship between individual characters and the tree is lost in the process of reducing characters to distances. These methods do not use character data directly, and information locked in the distribution of character states can be lost in the pairwise comparisons. Also, some complex phylogenetic relationships may produce biased distances. On any phylogram, branch lengths will be underestimated because some changes cannot be discovered at all due to failure to sample some species due to either experimental design or extinction (a phenomenon called the node density effect). However, even if pairwise distances from genetic data are "corrected" using stochastic models of evolution as mentioned above, they may more easily sum to a different tree than one produced from analysis of the same data and model using maximum likelihood. This is because pairwise distances are not independent; each branch on a tree is represented in the distance measurements of all taxa it separates. Error resulting from any characteristic of that branch that might confound phylogeny (stochastic variability, change in evolutionary parameters, an abnormally long or short branch length) will be propagated through all of the relevant distance measurements. The resulting distance matrix may then better fit an alternate (presumably less optimal) tree. Despite these potential problems, distance methods are extremely fast, and they often produce a reasonable estimate of phylogeny.