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Phylogeneticists are increasingly assembling genome-scale data sets that include hundreds of genes to resolve their focal clades. Although these data sets commonly include a moderate to high amount of missing data, there remains no consensus on their impact to species tree estimation. Here, using several simulated and empirical data sets, we assess the effects of missing data on species tree estimation under varying degrees of incomplete lineage sorting (ILS) and gene rate heterogeneity. We demonstrate that concatenation (RAxML), gene-tree-based coalescent (ASTRAL, MP-EST, and STAR), and supertree (matrix representation with parsimony [MRP]) methods perform reliably, so long as missing data are randomly distributed (by gene and/or by species) and that a sufficiently large number of genes are sampled. When data sets are indecisive sensu Sanderson et al. (2010. Phylogenomics with …