Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

The rapidly declining biosphere integrity, representing one of the core planetary boundaries,
is alarming. In particular, the global numbers of mammals, birds, fishes, and plants declined …

Despite the increasing popularity of Bayesian inference in empirical research, few practical
guidelines provide detailed recommendations for how to apply Bayesian procedures and …

This review summarizes the current state of the art of statistical and (survey) methodological
research on measurement (non) invariance, which is considered a core challenge for the …

Given that an effect size of d=. 4 is a good first estimate of the smallest effect size of interest
in psychological research, we already need over 50 participants for a simple comparison of …

Although the statistical tools most often used by researchers in the field of psychology over
the last 25 years are based on frequentist statistics, it is often claimed that the alternative …

As Bayesian methods continue to grow in accessibility and popularity, more empirical
studies are turning to Bayesian methods to model small sample data. Bayesian methods do …

Small-sample inference with clustered data has received increased attention recently in the
methodological literature, with several simulation studies being presented on the small …

In small sample contexts, Bayesian estimation is often suggested as a viable alternative to
frequentist estimation, such as maximum likelihood estimation. Our systematic literature …