 Long-branch attraction refers to the tendency of some methods to preferentially group together long branches (those with more evolutionary changes) regardless of their actual history. Methods designed to identify trees requiring the fewest number of evolutionary changes to explain the data will prefer explanations of shared ancestry over convergent evolution—even in cases where convergent evolution occurred. For example, imagine two branches with rapidly evolving DNA sequences. By chance alone, the two branches might have the same mutation at the same site which parsimony treats as evidence that these branches share a recent history. In a range of cases, the chances of these similar mutations occurring is so high that we expect the number of these convergent cases to overwhelm the signal from the true homologies. In these cases, the parsimony method is statistically inconsistent. A method is statistically consistent when it is guaranteed, as more data are added, to identify the correct solution. Methods that fail to do this are statistically inconsistent. In this case, not only does parsimony fail to be consistent, but the method reliably returns increased support for a specific, incorrect outcome as more data are added and hence is “positively misleading”.