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Phylogenetic comparative methods (PCMs), especially ones based on linear models, have played a central role in understanding species’ trait evolution. These methods, however, usually assume that phylogenetic trees are known without error or uncertainty, but this assumption is most likely incorrect. So far, Markov chain Monte Carlo (MCMC)-based Bayesian methods have mainly been deployed to account for such “phylogenetic uncertainty” in PCMs. Herein, we propose an approach with which phylogenetic uncertainty is incorporated in a simple, readily implementable and reliable manner. Our approach uses Rubin’s rules, which are an integral part of a standard multiple imputation procedure, often employed to recover missing data. We see true phylogenetic trees as missing data under this approach. Further, unmeasured species in comparative data (i.e., missing trait data) can be seen as another source of …