title: Why Bayesian Psychologists Should Change the Way They Use the Bayes Factor.
abstract: The discussion following Bem's ( 2011 ) psi research highlights that applications of the Bayes factor in psychological research are not without problems. The first problem is the omission to translate subjective prior knowledge into subjective prior distributions. In the words of Savage ( 1961 ): "they make the Bayesian omelet without breaking the Bayesian egg." The second problem occurs if the Bayesian egg is not broken: the omission to choose default prior distributions such that the ensuing inferences are well calibrated. The third problem is the adherence to inadequate rules for the interpretation of the size of the Bayes factor. The current paper will elaborate these problems and show how to avoid them using the basic hypotheses and statistical model used in the first experiment described in Bem ( 2011 ). It will be argued that a thorough investigation of these problems in the context of more encompassing hypotheses and statistical models is called for if Bayesian psychologists want to add a well-founded Bayes factor to the tool kit of psychological researchers.
authors.affiliation: a Department of Methods and Statistics , Utrecht University.|b CITO Institute for Educational Measurement.
authors.forename: Herbert
authors.initials: H
authors.lastname: Hoijtink
authors.affiliation: a Department of Methods and Statistics , Utrecht University.
authors.forename: Pascal
authors.initials: P
authors.lastname: van Kooten
authors.affiliation: a Department of Methods and Statistics , Utrecht University.
authors.forename: Koenraad
authors.initials: K
authors.lastname: Hulsker
mesh_terms: D001499:Bayes Theorem; D035841:Behavioral Research / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011584:Psychology / Q000379:methods*
keywords: Bayes factor; calibrated Bayes; default prior distributions; frequency calculations; subjective prior distribution