Open AccessSimulation Studies as a Tool to Understand Bayes Factors
Author(s) -
Don van Ravenzwaaij,
Alexander Etz
Publication year - 2021
Publication title -
advances in methods and practices in psychological science
Language(s) - English
Resource type - Journals
eISSN - 2515-2467
pISSN - 2515-2459
DOI - 10.1177/2515245920972624
Subject(s) - bayes' theorem , bayes factor , computer science , simple (philosophy) , set (abstract data type) , phenomenon , factor (programming language) , machine learning , artificial intelligence , bayesian probability , programming language , epistemology , philosophy
When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.