Table of Contents

Dedication
Acknowledgments
Prelude
1: Encountering Data
2: R and Exploratory Data Analysis
3: The Line of Best Fit
4: Probability and Random Variables
5: Properties of Random Variables
Interlude
6: Properties of Point Estimators
7: Interval Estimation and Inference
8: Semiparametric Estimation and Inference
9: Parametric Estimation and Inference
10: Bayesian Estimation and Inference
Postlude: Models and Data
Appendix A: A Tour of Calculus
Appendix B: More R Details
Appendix C: Answers to Exercises
Table of mathematical notation
Glossary
Bibliography
Index

About the Author

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.

Reviews

This book is extraordinarily accessible. It is engaging, very good, and deserves wider recognition as a course text for advanced undergraduate level or beginning science research graduate students. What really makes it a compelling course (and self-learning) text are the many exercises scattered throughout. This is a very practical text whose main aim is to increase the statistical expertise of users. Throughout, the reader is treated to a lively, witty and engaging writing style. Highly recommended.
*Journal of the Royal Statistical Society*

Statistical Thinking from Scratch: A Primer for Scientists, a new book by M.D. Edge, a population geneticist, fills a unique niche in this landscape, sitting between the inference-focused material most biodiversity scientists are likely familiar with, and mathematical statistics books that focus on the derivations and properties of estimators.