Bayesian data analysis is superseding traditional methods in sciences from anthropology to zoology. Bayesian methods solve many problems inherent in p values and confidence intervals. More importantly, Bayesian methods are more richly and intuitively informative. Bayesian analysis applies flexibly and seamlessly to simple situations or complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic and psychometric models. This full-day tutorial presents a ground-level, hands-on introduction to doing Bayesian data analysis. The presenter is an award-winning teacher who has honed new materials from many previous courses. He has written an acclaimed textbook on the topic, now greatly expanded in its second edition. The tutorial materials include free software and numerous programs that can be used for real data analysis.