In virtually every field, researchers find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires much more than the rote application of traditional statistical methods. This book will train researchers to be creative and confident users of statistics by thinking hard about the application of simple methods to a small dataset. In particular, this book focuses on simple linear regression—a method with strong connections to the most important tools in applied statistics—using it as a detailed case study for teaching resampling-based, likelihood-based, and Bayesian approaches to statistical inference. This exercise imparts an idea of how statistical procedures are designed and implemented, a flavor for the philosophical positions one implicitly assumes when applying statistics, and an opportunity to probe the strengths and weaknesses of one’s statistical approach. Key to the book’s novel approach is its mathematical level, which is gentler than most texts for statisticians but more rigorous than most introductory texts for non-statisticians. Statistical Thinking from Scratch is suitable for senior undergraduate and beginning graduate students, professional researchers, and practitioners seeking to improve their understanding of statistical methods across the natural and social sciences, medicine, psychology, public health, business, and other fields.