Bayesian statistical methods have become more feasible to implement with advances in computing but are not commonly used in educational research. In contrast to frequentist approaches that take hypotheses (and the associated parameters) as fixed, Bayesian methods take data as fixed and hypotheses as random. This difference means that Bayesian inference can take the form of an intuitive probabilistic statement about the likelihood of a particular hypothesis being true. Frequentist results are sometimes framed this way, but this framing is incorrect and can be very misleading. Bayesian methods also allow the incorporation of prior information and can facilitate the systematic combination of evidence from multiple sources. The papers in this symposium explore the potential for using Bayesian methods in education research and decision-making. The first paper, "Why Bother With Bayes?" (Thomas Louis) provides an introduction to Bayesian methods and some examples of how they are used outside of education research. The second paper, "Comparing Bayesian and Frequentist Inference for Decision-Making" (Ignacio Martinez) presents results from an experimental study of how people interpret and use the results of a study of an educational technology when the results are presented with frequentist versus Bayesian framing. The third paper, "Simple Application of Bayesian Methods for School-Level Decisions" (Alexandra Resch) presents an application of simple Bayesian analyses to real-world education technology evaluations. The fourth paper, "What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Policy Analysis" (Jonathan Gellar) presents a more complex application of Bayesian analyses, using a simulation study to demonstrate that a Bayesian adaptive design can provide better inference with smaller samples. This symposium summary provides abstracts for each of the four papers. [SREE documents are structured abstracts of SREE conference symposium, panel, and paper or poster submissions.]