Literature DB >> 20542462

What to believe: Bayesian methods for data analysis.

John K Kruschke1.   

Abstract

Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2010        PMID: 20542462     DOI: 10.1016/j.tics.2010.05.001

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


  57 in total

1.  Newborn chickens generate invariant object representations at the onset of visual object experience.

Authors:  Justin N Wood
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-05       Impact factor: 11.205

2.  Significance testing as perverse probabilistic reasoning.

Authors:  M Brandon Westover; Kenneth D Westover; Matt T Bianchi
Journal:  BMC Med       Date:  2011-02-28       Impact factor: 8.775

Review 3.  A meta-analysis of the survival-processing advantage in memory.

Authors:  John E Scofield; Erin M Buchanan; Bogdan Kostic
Journal:  Psychon Bull Rev       Date:  2018-06

4.  A causal account of the brain network computations underlying strategic social behavior.

Authors:  Christopher A Hill; Shinsuke Suzuki; Rafael Polania; Marius Moisa; John P O'Doherty; Christian C Ruff
Journal:  Nat Neurosci       Date:  2017-07-10       Impact factor: 24.884

5.  Bayesian data analysis for newcomers.

Authors:  John K Kruschke; Torrin M Liddell
Journal:  Psychon Bull Rev       Date:  2018-02

6.  A Monte Carlo-Based Bayesian Approach for Measuring Agreement in a Qualitative Scale.

Authors:  Fernando Calle-Alonso; Carlos Javier Pérez Sánchez
Journal:  Appl Psychol Meas       Date:  2014-11-05

Review 7.  Using priors to formalize theory: optimal attention and the generalized context model.

Authors:  Wolf Vanpaemel; Michael D Lee
Journal:  Psychon Bull Rev       Date:  2012-12

Review 8.  Parafoveal preview effects from word N + 1 and word N + 2 during reading: A critical review and Bayesian meta-analysis.

Authors:  Martin R Vasilev; Bernhard Angele
Journal:  Psychon Bull Rev       Date:  2017-06

9.  P Value Problems.

Authors:  Samuel C Karpen
Journal:  Am J Pharm Educ       Date:  2017-11       Impact factor: 2.047

10.  Reaction time in ankle movements: a diffusion model analysis.

Authors:  Konstantinos P Michmizos; Hermano Igo Krebs
Journal:  Exp Brain Res       Date:  2014-07-17       Impact factor: 1.972

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.