Abstract

We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests lack; in particular, the Bayesian test can quantify evidence in favor of the null hypothesis and allows researchers to monitor the test results as the data come in. We illustrate the use of the Bayesian correlation test with three examples from the psychological literature. Computer code and example data are provided in the journal archives.

Highlights

  • A correlation coefficient indicates how strongly two variables are related

  • The first correlation coefficient was developed by Francis Galton in 1888 (Stigler, 1989); further work by Francis Edgeworth and Karl Pearson resulted in the correlation measure that is used most frequently today, the Pearson product–moment correlation coefficient, or r (Pearson, 1920)

  • We focus on the two-sided hypothesis test for the Pearson correlation coefficient

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Summary

Introduction

A correlation coefficient indicates how strongly two variables are related. The concept is basic, and it comes as no surprise that the correlation coefficient ranks among the most popular statistical tools in any subfield of psychological science. The first correlation coefficient was developed by Francis Galton in 1888 (Stigler, 1989); further work by Francis Edgeworth and Karl Pearson resulted in the correlation measure that is used most frequently today, the Pearson product–moment correlation coefficient, or r (Pearson, 1920). The coefficient r is a measure of the linear relation between two variables, where r 0 −1 indicates a perfectly negative linear relation, r 0 1 indicates a perfectly positive relation, and r 0 0 indicates the absence of any linear relation.

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