 In response, researchers have developed heuristic search strategies to explore possible tree space. For example, in Bayesian analysis the probability space of possible trees is so large that starting by randomly selecting a tree will be highly inefficient. It will almost surely fail to provide a good estimate of the posterior probability distribution, since the interesting regions will only occupy a small part of that distribution space (Ronquist et al. 2009). A common heuristic is to start with a parsimony or distance-methods tree. Though these methods typically perform worse than model-based approaches, they are much less computationally expensive and can quickly generate an initially plausible tree that Bayesian phylogeneticists use as a launching off point to explore the tree probability space (e.g., using hill-climbing algorithms such as Markov Chain Monte Carlo (MCMC) sampling). Innovations like these are one reason that computational phylogenetics is an important field in both statistics and computer science. Philosophers of science interested in how scientists explore and use so-called “Big Data” would profit from examining how phylogeneticists have developed, adapted, and employed heuristic search strategies.