Journal ArticleDOI
Statistical Evidence in Experimental Psychology An Empirical Comparison Using 855 t Tests
Ruud Wetzels,Dora Matzke,Michael D. Lee,Jeffrey N. Rouder,Geoffrey J. Iverson,Eric-Jan Wagenmakers +5 more
TLDR
The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology and conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.Abstract:
Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary measures of evidence, such as effect sizes. The second is to replace inference with Bayesian measures of evidence, such as the Bayes factor. The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology. The comparison yields two main results. First, although p values and default Bayes factors almost always agree about what hypothesis is better supported by the data, the measures often disagree about the strength of this support; for 70% of the data sets for which the p value falls between .01 and .05, the default Bayes factor indicates that the evidence is only anecdotal. Second, effect sizes can provide additional evidence to p values and default Bayes factors. The authors conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.read more
Citations
More filters
Journal ArticleDOI
Using Bayes to get the most out of non-significant results
TL;DR: It is argued Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches, and provides a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive.
Journal ArticleDOI
Default Bayes factors for ANOVA designs
TL;DR: Bayes factors have been advocated as superior to pp-values for assessing statistical evidence in data as mentioned in this paper, and they have been widely used in the literature for assessing power law and skill acquisition.
Journal ArticleDOI
Bayesian Estimation Supersedes the t Test
TL;DR: Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their Difference, and the normality of the data.
Journal ArticleDOI
Bayesian inference for psychology. Part II: Example applications with JASP
Eric-Jan Wagenmakers,Jonathon Love,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Ravi Selker,Quentin Frederik Gronau,Damian Dropmann,Bruno Boutin,Frans Meerhoff,Patrick Knight,Akash Raj,Erik-Jan van Kesteren,Johnny van Doorn,Martin Šmíra,Sacha Epskamp,Alexander Etz,Dora Matzke,Tim de Jong,Don van den Bergh,Alexandra Sarafoglou,Helen Steingroever,Koen Derks,Jeffrey N. Rouder,Richard D. Morey +25 more
TL;DR: This part of this series introduces JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems.
Journal ArticleDOI
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications.
Eric-Jan Wagenmakers,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Jonathon Love,Ravi Selker,Quentin Frederik Gronau,Martin Šmíra,Sacha Epskamp,Dora Matzke,Jeffrey N. Rouder,Richard D. Morey +12 more
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
References
More filters
Book
Statistical Power Analysis for the Behavioral Sciences
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Book
Data Analysis Using Regression and Multilevel/Hierarchical Models
Andrew Gelman,Yu-Sung Su +1 more
TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.
Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
Journal ArticleDOI
Bayesian data analysis.
TL;DR: A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented.
Journal ArticleDOI
WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
TL;DR: How and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design are discussed and how the framework may be extended.