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 …

For almost half a century, Paul Meehl educated psychologists about how the mindless use of
null-hypothesis significance tests made research on theories in the social sciences basically …

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial
designs. Typically, ANOVAs are executed using frequentist statistics, where p-values …

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

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …

We illustrate the Bayesian approach to data analysis using the newly developed statistical
software program JASP. With JASP, researchers are able to take advantage of the benefits …

The current paper highlights a new, interactive Shiny App that can be used to aid in
understanding and teaching the important task of conducting a prior sensitivity analysis …

Applauded for its clarity, this accessible introduction helps readers apply multilevel
techniques to their research. The book also includes advanced extensions, making it useful …

This book introduces multiple-latent variable models by utilizing path diagrams to explain
the underlying relationships in the models. This approach helps less mathematically inclined …