What Are the Odds? A Practical Guide to Computing and Reporting Bayes Factors

TLDR

The Bayes factor, like the p‑value, enables statistical inference from experimental data but uniquely quantifies evidence for the alternative hypothesis and offers clearer evidence estimates, building on prior work by Wagenmakers, Rouder, and Masson. This paper offers a concise template and practical guide for incorporating Bayes factors into experimental reporting, especially for Journal of Problem Solving articles, with examples from published problem‑solving studies.

Abstract

The purpose of this paper is to provide an easy template for the inclusion of the Bayes factor in reporting experimental results, particularly as a recommendation for articles in the Journal of Problem Solving. The Bayes factor provides information with a similar purpose to the p-value – to allow the researcher to make statistical inferences from data provided by experiments. While the p-value is widely used, the Bayes factor provides several advantages, particularly in that it allows the researcher to make a statement about the alternative hypothesis, rather than just the null hypothesis. In addition, it provides a clearer estimate of the amount of evidence present in the data. Building on previous work by authors such as Wagenmakers (2007), Rouder et al. (2009), and Masson (2011), this article provides a short introduction to Bayes factors, before providing a practical guide to their computation using examples from published work on problem solving.