Selection bias is a persistent, and often hidden, problem in educational research. It is the primary obstacle standing in between increasingly available large education datasets and the ability to make valid causal inferences to inform policymaking, research, and practice (Stuart, 2010). This article provides an accessible discussion on the importance of understanding selection bias in educational research. Although a general explanation on how to remove selection bias is beyond the scope of this article, the reader is guided through an example of this removal process. Specifically, a propensity score analysis is used on a nationally representative dataset to examine whether high school course taking in the algebra-calculus pipeline has a causal effect on placing out of postsecondary remedial mathematics. Several visualizations of the selection bias, and the process of its removal, are provided to give readers a sense of its impact on analyzing observational data.