Entrepreneurship involves recognizing, analyzing, and exploiting perceived opportunities, which, in turn, lead to product, organization, and industry creation (Brush et al., 2003; Shane and Venkataraman, 2000). To examine this process, entrepreneurship scholars have examined critical research questions such as “how do entrepreneurs make decisions under conditions of uncertainty?” and “how does additional entrepreneurial experience impact decision making?” (Baron and Ensley, 2006; Parker, 2006; Saravarthy and Berglund, 2010). Similar to most organizational research, when studying these issues empirically, entrepreneurship scholars have primarily employed p-value null hypothesis significance testing (pNHST) methods (Dean et al., 2007). Increasingly, however, some organizational researchers have suggested that methods based on other approaches might help advance the field by incorporating different assumptions and methods into empirical analyses. For example, pNHST-based studies often employ group means as part of their calculations, which may statistically neutralize important differences among individuals or organizations that scholars seek to explain (e.g. Hansen et al., 2004). In addition, researchers cannot employ pNHST methods to compare support for one theoretical model versus another because p-values only provide evidence to support or reject the null hypothesis (Andraszewicz et al., 2015). Thus, other methods that overcome these potential limitations may be needed to study critical entrepreneurship issues. Bayesian analysis represents one such set of methods, and scholars in other business (e.g. management science, marketing, and finance) fields have increasingly employed these methods to study issues like decision making. For example, Allenby et al. (2004) found over 50 articles published in top marketing journal that examined Bayesian methods issues during the 1990s. Given its many advantages, Bayesian methods provide another important tool for organizational scholars, in general (Kruschke et al., 2012), and, as we detail below, entrepreneurship researchers, in particular. Most importantly, Bayesian analysis enables scholars to gauge how decision makers update their estimated probabilities of potential outcomes as new data become available, making it a useful method for studying decision making throughout the entrepreneurial process. In addition, Bayesian analysis employs previous results as an input (i.e. “prior beliefs”), and it faces fewer restrictions on sample size than pNHST-based methods (Zyphur and Oswald, 2013). To date, however, Bayesian methods have seen only limited use in entrepreneurship research, and, as our review below shows, extant research has mostly been conceptual or employed simulated data. Even this limited research, however, has shown the value of employing these methods in studying entrepreneurship processes and topics (Block et al., 2014).