Videos

Prof. Kruschke hopes to create short videos that highlight aspects of chapters in the book. The videos will be added here as they become available. Also listed here are videos from conference talks.

Some Bayesian approaches to replication analysis and planning. A talk presented at the Association for Psychological Science, Friday May 27, 2016. This video is a pre-recording while sitting at my desk; the actual talk included some spontaneous additions about relations of the material to previous speakers' talks, and a few attempts at you-had-to-be-there humor. For a snapshot of the speakers, see http://doingbayesiandataanalysis.blogspot.com/2016/05/some-bayesian-approaches-to-replication.html

Comment at YouTube.

Bayesian estimation supersedes the t test. Highlights from the JEP:General article of the same title. Talk presented at the Psychonomic Society, Nov. 2012. Contents include a brief overview of Bayesian estimation for two groups, and three cases contrasting Bayesian estimation with the classic t test.

Bayesian methods interpret data better. Contents include a very brief overview of Bayesian estimation and decision rules, then a look at sequential testing of accumulating data, the goal of precision (as opposed to rejecting null), and multiple comparisons (with shrinkage in hierarchical models). Talk at Psychonomic Society Special Session, Nov. 2012. For a much more developed version of these ideas, see the video, Precision as a Goal for Data Collection, below.

Precision as a goal for data collection. Talk at U.C. Irvine, March 14, 2014. Part 1: Rejecting null is not enough, we also need an estimate and its precision. Bayesian estimation supersedes frequentist confidence intervals and "the new statistics". Part 2: Bayesian estimation supersedes "the new statistics." @2:35: Two Bayesian ways to assess a null value. Highest density interval with region of practical equivalence. Bayesian model comparison and Bayes factor. Part 3: More on two Bayesian methods to assess null values. @5:45: Biased estimation in sequential testing and optional stopping. Part 4: Monte Carlo study of biased estimation in sequential testing and optional stopping.