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Bayes factors can distinguish evidence of absence from absence of evidence, unlike frequentist statistics. A new, simple method helps researchers specify effect sizes, improving Bayes factor usability in psychology research.

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Area of Science:

  • Psychology
  • Statistical Methods

Background:

  • Frequentist statistics in psychology cannot differentiate between evidence of absence and absence of evidence.
  • Bayes factors offer a solution but face challenges in specifying effect sizes and have a steep learning curve.

Purpose of the Study:

  • To present a simple method for generating plausible effect size ranges (Hypothesis 1 models) for binary-dependent variables.
  • To demonstrate the utility of this method using a case study and simulations.

Main Methods:

  • Utilized estimates from frequentist logistic mixed-effects models.
  • Employed Bayesian model comparison with Bayesian hierarchical models for increased flexibility.
  • Generated a range of plausible effect sizes for Hypothesis 1.

Main Results:

  • Bayes factors calculated using the proposed method yielded intuitively reasonable results.
  • The method simplifies the specification of effect sizes, addressing a key barrier to Bayes factor adoption.
  • Demonstrated effectiveness across a range of real effect sizes.

Conclusions:

  • The presented method enhances the practical application of Bayes factors in psychological research.
  • This approach facilitates a clearer interpretation of statistical evidence, particularly for the absence of an effect.
  • Encourages wider adoption of Bayes factors for more nuanced statistical inference.