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A Good check on the Bayes factor.

Nikola Sekulovski¹, Maarten Marsman², Eric-Jan Wagenmakers²

¹Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. n.sekulovski@uva.nl.

Behavior Research Methods

|September 4, 2024

Summary

This summary is machine-generated.

This study validates Bayes factor calculations using Turing and Good

Keywords:

Bayesian hypothesis testing Turing Weight of evidence

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Area of Science:

Statistics
Computational Statistics

Background:

Bayes factor hypothesis testing is crucial for comparing models.
Standard methods for Bayes factor calculation can be complex.
Ensuring computational soundness is vital for reliable results.

Purpose of the Study:

To validate Bayes factor calculations using established theorems.
To provide a method for checking the accuracy of Bayes factor computations.
To enhance the trustworthiness of statistical inferences.

Main Methods:

Simulating datasets under competing hypotheses.
Calculating Bayes factors for simulated data.
Applying theorems by Alan Turing and Jack Good for validation.

Main Results:

The expected values of Bayes factors aligned with theoretical predictions.
The method successfully detected computational errors.
Demonstrated computational correctness in ANOVA and network psychometrics.

Conclusions:

The proposed validation method is effective for Bayes factor computations.
This approach enhances the credibility of Bayes factor hypothesis testing.
Facilitates more robust and reliable scientific conclusions.