 Maximum likelihood estimation is known to be provably consistent under a wide variety of conditions (Wald 1949), but several authors have argued that these conditions do not apply to estimating the tree topology since tree topologies are discrete, not continuous, parameters (Yang 1996; Siddall 1998; Farris 1999). However, Swofford et al. (2001) argue that Wald’s conditions do apply and Yang (1994), Chang (1996), and Rogers (2001) prove that maximum likelihood is consistent under different assumptions about character evolution. But regardless of which assumptions suffice for likelihood to be consistent, some assumptions about the evolutionary process are definitely required. But what happens when the model we use is not the correct model? This is especially problematic when combined with the view that to be a correct model it should be true, and that we could never know that our model was true or perhaps even that there is no such thing as a true model, as some pattern cladists have claimed (e.g., Brower 2016, 2018b). As Felsenstein (2004: 272) puts it,