Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary outcomes: a case study under practical design constraints

doi:10.3389/fpsyg.2026.1770212

Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary outcomes: a case study under practical design constraints

2026-04-17

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DOI：10.3389/fpsyg.2026.1770212

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Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology; research questions are often addressed using Null Hypothesis Significance Testing (NHST); an approach rooted in the frequentist statistical tradition. In scenarios that do not consent to reject the null hypothesis using the NHST paradigm (i.e.; results are non-significant); researchers may be tempted to reframe their analysis in the Bayesian framework; either as a complementary alternative or alongside the original NHST approach. In fact; the Bayesian approach is gaining increasing appeal in the social sciences as an alternative to the frequentist NHST framework; and Bayesian methods for hypothesis testing (i.e.; the Bayes Factor) can be used to help determine whether a failure to reject the null hypothesis reflects merely insufficient evidence for the alternative hypothesis or provides affirmative evidence for the (point) null hypothesis. Nevertheless; using the two approaches interchangeably carries the risk of conceptual confusion; as NHST and Bayesian frameworks address different inferential questions. This study provides an empirical; real-world opportunity to examine how NHST and Bayesian methods can be applied to the same hypothesis test when using Generalized Linear Mixed Models with a Binary Outcome. Importantly; this application incorporates common experimental constraints into the design-analysis planning; defining a reachable; realistic albeit underpowered sample size; assuming the classical 0.80 power threshold. This research report provides a valuable opportunity to examine how Bayesian and NHST approaches potentially differ in their workflow; performance; and inferential interpretation under realistic experimental conditions.

Keywords:

Bayesian analysis

moral dilemmas

Bayes Factor

NHST analysis

underpowered designs

Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary outcomes: a case study under practical design constraints

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