Frequentist vs. Bayesian methods: Choosing appropriate statistical methods in second language research

Abstract

Null hypothesis significance testing (NHST) with p-values is one of the most commonly used statistical procedures in second language research. This statistical approach follows the principles of the frequentist method, and although it has various advantages, some researchers have noted its limitations and proposed Bayesian methods as an alternative. To contribute to this debate, this article introduces the basic principles of Bayesian statistics, specifically Bayesian hypothesis testing (BHT), and explores its advantages and limitations compared to the frequentist approach, particularly NHST. The article first outlines the foundational concepts of NHST and reviews the main criticisms associated with its use. It then presents the core ideas of Bayesian methods, with a primary focus on the Bayes factor, followed by a description of general procedures for conducting BHT and an overview of its potential benefits in applied research contexts. Additionally, several challenges and criticisms of Bayesian methods are discussed, emphasizing that they are not always a superior alternative. Based on these discussions, the article argues that both frequentist and Bayesian methods have strengths and limitations, and that specific research goals, questions, and contexts should guide the choice of statistical framework.