appropriate to say that parsimony assumes only the minimum amount of change implied by the data. As above, this does not require that these were the only changes that occurred; it simply does not infer changes for which there is no evidence. The shorthand for describing this, to paraphrase Farris is that "parsimony minimizes assumed homoplasies, it does not assume that homoplasy is minimal." Recent simulation studies suggest that parsimony may be less accurate than trees built using Bayesian approaches for morphological data, potentially due to overprecision, although this has been disputed. Studies using novel simulation methods have demonstrated that differences between inference methods result from the search strategy and consensus method employed, rather than the optimization used. Also, analyses of 38 molecular and 86 morphological empirical datasets have shown that the common mechanism assumed by the evolutionary models used in model-based phylogenetics apply to most molecular, but few morphological datasets. This finding validates the use of model-based phylogenetics for molecular data, but suggests that for morphological data, parsimony remains advantageous, at least until more sophisticated models become available for phenotypic data. Alternatives There are several other methods for inferring phylogenies based on discrete character data, including maximum likelihood and Bayesian inference. Each offers potential advantages and disadvantages. In practice, these methods tend to favor trees that are very similar to the most parsimonious tree(s) for the same dataset; however, they allow for complex modelling of evolutionary processes, and as classes of methods are statistically consistent and are not susceptible to long-branch attraction. Note, however, that the performance of likelihood and Bayesian methods are dependent on the quality of the particular model of evolution employed; an incorrect model can produce a biased result - just like parsimony. In addition, they are still quite computationally slow relative to parsimony methods, sometimes requiring weeks to run large datasets. Most of these methods have particularly avid proponents and detractors; parsimony especially has been advocated as philosophically superior (most notably by ardent cladists). One area where parsimony still holds much sway is in the analysis of morphological data, because—until recently—stochastic models of character change were not available for non-molecular data, and they are still not widely implemented. Parsimony has also recently been shown to be more likely to recover the true tree in the face of profound changes in evolutionary ("model") parameters (e.g., the rate of evolutionary change) within a tree. Distance matrices can