Bayesian Data Analysis, APS 2011

## Workshop: Doing Bayesian Data Analysis

### Convention of the Association for Psychological Science, Thursday May 26, 2011, 9:00am-10:50am, Room: Lincoln East.

 Figure 1. Why you should attend the tutorial. (Notice that the Bayesian analysis reveals many credible regression lines, for which the slopes and intercepts trade off, instead of just one "best" line.)

This two-hour workshop shows you how to do Bayesian data analysis, hands on (with free software called R and BUGS). The intended audience is grad students and other researchers who want a ground-floor introduction to Bayesian data analysis. No mathematical expertise is presumed.

Agenda: The two-hour workshop will rocket through the following topics, allocating about a half hour to each.

• Why you should be embarrassed to report p values and why you should be proud to do Bayesian analysis instead. More on reasons to go Bayesian below.
• Bayes' rule, grid approximation, and R.
• Markov chain Monte Carlo and BUGS. This does not involve any physical restraints or insects. Unfortunately it also does not involve Monte Carlo.
• Linear regression and hierarchical models of individual differences. E.g., Figure 1.
• Brief look at Bayesian multiple linear regression and Bayesian analysis of variance.

Bayesian data analysis is not Bayesian modeling of cognition. Data analysis involves "generic" descriptive models (such as linear regression) without any necessary interpretation as cognitive computation. The rational way to estimate parameters in descriptive models is Bayesian, regardless of whether or not Bayesian models of mind are viable. The concepts and methods of Bayesian data analysis transfer to other Bayesian models, including Bayesian models of cognition.

Why go Bayesian? See Figure 1. But beyond that, sciences from astronomy to zoology are changing from 20th-century null-hypothesis significance testing to Bayesian data analysis. Read more:

• An open letter explaining why it's time to go Bayesian.
• An article* that explains a critical flaw of p-values in null hypothesis significance testing, and two different Bayesian approaches to assessing null values.
Kruschke, J. K. (2010). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5), 658-676. (doi:10.1002/wcs.72)
• An article* that emphasizes advantages of Bayesian data analysis and the fact that Bayesian data analysis is appropriate regardless of the status of Bayesian models of cognition.
Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293-300. (doi:10.1016/j.tics.2010.05.001)
*Your click on this link constitutes your request to the author for a personal copy of the article exclusively for individual research.

Who is the instructor? John Kruschke is five-time winner of Teaching Excellence Recognition Awards from Indiana University, where he is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics. He has written an introductory textbook on Bayesian data analysis; see also the articles linked above. His research interests include the science of morality, applications of Bayesian methods to teaching and learning, and models of attention in learning, which he has developed in both connectionist and Bayesian formalisms. He received the Troland Research Award from the National Academy of Sciences.