However, the relative novelty of conducting Bayesian analyses in applied fields means that there are no detailed reporting standards, and this in turn may frustrate the broader adoption and proper interpretation of the Bayesian framework. For the pragmatic researcher, the adoption of the Bayesian framework brings several advantages over the standard framework of frequentist null-hypothesis significance testing (NHST), including (1) the ability to obtain evidence in favor of the null hypothesis and discriminate between “absence of evidence” and “evidence of absence” (Dienes, 2014 Keysers, Gazzola, & Wagenmakers, 2020) (2) the ability to take into account prior knowledge to construct a more informative test (Gronau, Ly, & Wagenmakers, 2020 Lee & Vanpaemel, 2018) and (3) the ability to monitor the evidence as the data accumulate (Rouder, 2014). In recent years, Bayesian inference has become increasingly popular, both in statistical science and in applied fields such as psychology, biology, and econometrics (e.g., Andrews & Baguley, 2013 Vandekerckhove, Rouder, & Kruschke, 2018).
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