Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

TLDR

Bayesian parameter estimation and hypothesis testing offer attractive alternatives to classical inference using confidence intervals and p‑values. Part I outlines ten prominent advantages of the Bayesian approach. The paper explains how Bayesian hypothesis testing quantifies evidence and tracks its evolution as data accrue, addresses common objections, and introduces JASP, a free open‑source tool for Bayesian estimation and testing across popular scenarios. These advantages translate into concrete opportunities for pragmatic researchers. This issue.

Abstract

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).