Statistical Thinking from Scratch: A Primer for Scientists

Researchers across the natural and social sciences find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires more than the rote application of formulaic statistical methods. The premise of Statistical Thinking from Scratch is that students who want to become confident data analysts are better served by a deep introduction to a single statistical method than by a cursory overview of many methods.

In particular, this book focuses on simple linear regression-a method with close 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. Considering simple linear regression in depth imparts an idea of how statistical procedures are designed, a flavour for the philosophical positions one assumes when applying statistics, and tools to probe the strengths 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.

M. D. Edge, Postdoctoral Researcher, Department of Evolution and Ecology, University of California, Davis, USA

M. D. Edge is a Postdoctoral Researcher in the Department of Evolution and Ecology at the University of California, Davis. Starting in 2020, he will be an Assistant Professor of Biological Sciences in the Quantitative and Computational Biology section at the University of Southern California.