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This textbook bypasses the need for advanced mathematics by providing intext 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 pvalues and null hypothesis testing, using the full power of modern probability theory to solve realworld problems. The book offers a fully selfcontained 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 realworld applications.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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