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

Area of Science:

• Statistics
• Psychological Science
• Data Analysis

Background:

• Classical statistical inference relies on confidence intervals and p-values.
• Bayesian inference offers an alternative framework for data analysis.
• Previous work has highlighted the benefits of Bayesian methods.

Purpose of the Study:

• To outline the advantages of Bayesian parameter estimation and hypothesis testing.
• To demonstrate how Bayesian methods address practical research needs.
• To counter common objections to Bayesian hypothesis testing.

Main Methods:

• Review and enumeration of ten key advantages of the Bayesian approach.
• Conceptual explanation of Bayesian hypothesis testing.
• Discussion of objections to Bayesian hypothesis testing.

Main Results:

• Bayesian hypothesis testing enables quantification of evidence.
• Evidence can be monitored as data accumulate, irrespective of data collection intentions.
• Ten prominent advantages of the Bayesian approach are detailed.

Conclusions:

• Bayesian methods provide a pragmatic and flexible alternative to classical inference.
• Bayesian hypothesis testing offers distinct benefits for researchers.
• Further resources (JASP software) facilitate the application of Bayesian methods.