Reasoning with Data An Introduction to Traditional and Bayesian Statistics Using R

Clear, engaging, and approachable, this book empowers readers to apply inferential statistical thinking to evaluate evidence, test assumptions, and avoid drawing misleading conclusions. It offers a practical, step-by-step introduction to both frequentist and Bayesian methods, allowing readers to compare the two perspectives across key statistical techniques—such as hypothesis testing, replication, ANOVA, effect size estimation, regression, time series analysis, and more.

Aimed at students from diverse backgrounds—including those without a strong math foundation—the book introduces the R programming language and essential packages for real-world data analysis. Each concept is paired with simple R commands and real-data examples, making statistical skills easy to apply and understand.

The companion website features annotated R scripts, in-class exercises, instructor resources, and slide presentations to support learning and teaching.

Key Features:

• Conversational tone and step-by-step explanations ideal for beginners

• Side-by-side presentation of frequentist and Bayesian methods

• End-of-chapter exercises using real-world datasets included in a free R package

• Clearly marked technical boxes for deeper exploration of equations and outputs

• Appendices with instructions for installing R and accessing sample datasets

Perfect for students and professionals looking to build a solid foundation in inference and data analysis using R—without the need for advanced math skills.