Bayesian inference in numerical cognition: A tutorial using JASP

Researchers in numerical cognition rely on hypothesis testing and parameter estimation to evaluate the evidential value of data. Though there has been increased interest in Bayesian statistics as an alternative to the classical, frequentist approach to hypothesis testing, many researchers remain hes...

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Published in:Journal of numerical cognition 2020-09, Vol.6 (2), p.231-259
Main Authors: Faulkenberry, Thomas J., Ly, Alexander, Wagenmakers, Eric-Jan
Format: Article
Language:English
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bayes factors
bayesian inference
jasp
numerical cognition
tutorial
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description Researchers in numerical cognition rely on hypothesis testing and parameter estimation to evaluate the evidential value of data. Though there has been increased interest in Bayesian statistics as an alternative to the classical, frequentist approach to hypothesis testing, many researchers remain hesitant to change their methods of inference. In this tutorial, we provide a concise introduction to Bayesian hypothesis testing and parameter estimation in the context of numerical cognition. Here, we focus on three examples of Bayesian inference: the t-test, linear regression, and analysis of variance. Using the free software package JASP, we provide the reader with a basic understanding of how Bayesian inference works “under the hood” as well as instructions detailing how to perform and interpret each Bayesian analysis.
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subjects bayes factors
bayesian inference
jasp
numerical cognition
tutorial
title Bayesian inference in numerical cognition: A tutorial using JASP
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