Bayesian Data Analysis for the Behavioral and Neural Sciences : Non-Calculus Fundamentals

This textbook teaches undergraduates in psychology, neuroscience, and medicine modern data analysis techniques. It uses non-calculus-based mathematics with examples specific to behavioral and neural sciences. Perfect for statistics courses, it shows students how to write code for their own data analyses, even those involving individual differences.

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.