Understanding the logic and interpretation of Bayes factors in psychological research
In short: Researchers who use Bayesian statistics often rely on a metric called the Bayes factor to evaluate evidence. This overview explains how these factors are calculated and how they differ from traditional frequentist methods. By clarifying the underlying logic, the authors aim to help scientists correctly interpret the strengths and weaknesses of this alternative statistical approach.
Because most psychological science education focuses on frequentist statistics, there is a need for clear resources on Bayesian methods. This paper provides a guide on setting priors in software, calculating factors, and interpreting results. However, as an overview article rather than an empirical study, it does not test new data but rather explains existing mathematical frameworks.
Why it matters: Understanding the logic behind Bayes factors helps researchers move beyond frequentist statistics to more nuanced evidence evaluation.
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