title: How to quantify the evidence for the absence of a correlation.
abstract: We present a suite of Bayes factor hypothesis tests that allow researchers to grade the decisiveness of the evidence that the data provide for the presence versus the absence of a correlation between two variables. For concreteness, we apply our methods to the recent work of Donnellan et al. (in press) who conducted nine replication studies with over 3,000 participants and failed to replicate the phenomenon that lonely people compensate for a lack of social warmth by taking warmer baths or showers. We show how the Bayes factor hypothesis test can quantify evidence in favor of the null hypothesis, and how the prior specification for the correlation coefficient can be used to define a broad range of tests that address complementary questions. Specifically, we show how the prior specification can be adjusted to create a two-sided test, a one-sided test, a sensitivity analysis, and a replication test.
authors.affiliation: Department of Psychology, University of Amsterdam, Weesperplein 4, 1018, XA, Amsterdam, The Netherlands. ej.wagenmakers@gmail.com.
authors.forename: Eric-Jan
authors.initials: EJ
authors.lastname: Wagenmakers
authors.affiliation: Department of Psychology, University of Amsterdam, Weesperplein 4, 1018, XA, Amsterdam, The Netherlands.
authors.forename: Josine
authors.initials: J
authors.lastname: Verhagen
authors.affiliation: Department of Psychology, University of Amsterdam, Weesperplein 4, 1018, XA, Amsterdam, The Netherlands.
authors.forename: Alexander
authors.initials: A
authors.lastname: Ly
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D013223:Statistics as Topic / Q000379:methods*
keywords: Bayes factor; Hypothesis test; Statistical evidence