title: Reply to Rouder (2014): good frequentist properties raise confidence.
abstract: Established psychological results have been called into question by demonstrations that statistical significance is easy to achieve, even in the absence of an effect. One often-warned-against practice, choosing when to stop the experiment on the basis of the results, is guaranteed to produce significant results. In response to these demonstrations, Bayes factors have been proposed as an antidote to this practice, because they are invariant with respect to how an experiment was stopped. Should researchers only care about the resulting Bayes factor, without concern for how it was produced? Yu, Sprenger, Thomas, and Dougherty (2014) and Sanborn and Hills (2014) demonstrated that Bayes factors are sometimes strongly influenced by the stopping rules used. However, Rouder (2014) has provided a compelling demonstration that despite this influence, the evidence supplied by Bayes factors remains correct. Here we address why the ability to influence Bayes factors should still matter to researchers, despite the correctness of the evidence. We argue that good frequentist properties mean that results will more often agree with researchers' statistical intuitions, and good frequentist properties control the number of studies that will later be refuted. Both help raise confidence in psychological results.
authors.affiliation: Department of Psychology, University of Warwick, Coventry, CV4 7AL, UK, A.N.Sanborn@warwick.ac.uk.
authors.forename: Adam N
authors.initials: AN
authors.lastname: Sanborn
authors.forename: Thomas T
authors.initials: TT
authors.lastname: Hills
authors.forename: Michael R
authors.initials: MR
authors.lastname: Dougherty
authors.forename: Rick P
authors.initials: RP
authors.lastname: Thomas
authors.forename: Erica C
authors.initials: EC
authors.lastname: Yu
authors.forename: Amber M
authors.initials: AM
authors.lastname: Sprenger
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012107:Research Design / Q000592:standards*