also be used to generate phylogenetic trees. Non-parametric distance methods were originally applied to phenetic data using a matrix of pairwise distances and reconciled to produce a tree. The distance matrix can come from a number of different sources, including immunological distance, morphometric analysis, and genetic distances. For phylogenetic character data, raw distance values can be calculated by simply counting the number of pairwise differences in character states (Manhattan distance) or by applying a model of evolution. Notably, distance methods also allow use of data that may not be easily converted to character data, such as DNA-DNA hybridization assays. Today, distance-based methods are often frowned upon because phylogenetically-informative data can be lost when converting characters to distances. There are a number of distance-matrix methods and optimality criteria, of which the minimum evolution criterion is most closely related to maximum parsimony. Minimum Evolution From among the distance methods, there exists a phylogenetic estimation criterion, known as Minimum Evolution (ME), that shares with maximum-parsimony the aspect of searching for the phylogeny that has the shortest total sum of branch lengths. A subtle difference distinguishes the maximum-parsimony criterion from the ME criterion: while maximum-parsimony is based on an abductive heuristic, i.e., the plausibility of the simplest evolutionary hypothesis of taxa with respect to the more complex ones, the ME criterion is based on Kidd and Sgaramella-Zonta's conjectures (proven true 22 years later by Rzhetsky and Nei) stating that if the evolutionary distances from taxa were unbiased estimates of the true evolutionary distances then the true phylogeny of taxa would have a length shorter than any other alternative phylogeny compatible with those distances. Rzhetsky and Nei's results set the ME criterion free from the Occam's razor principle and confer it a solid theoretical and quantitative basis. See also Informative site Occam's razor References Phylogenetics Computational phylogenetics