Explore the foundations of statistical thinking in this comprehensive lecture on Bayesian inference and probability theory. Delve into key concepts such as generative models, Bayesian updating, and the Garden of Forking Data metaphor. Learn about probability distributions, hypothesis testing, and the beta distribution for modeling infinite possibilities. Discover how to work with posterior distributions, perform sampling, and make predictions. Conclude with a summary of core principles and a bonus discussion on misclassification. Access accompanying course materials on GitHub to enhance your understanding of statistical rethinking.