# Preface

Nearly all research in the biological and environmental sciences relies on data analysis of some kind. Statistical literacy is therefore important, not just for doing research, but also for understanding and evaluating the research of other scientists. This book is an introductory textbook for learning statistics. It starts with the very basics and prioritises helping the reader develop a conceptual understanding of statistics and applying the most fundamental statistical tools. Mathematical details are generally placed in footnotes, and developing good statistical judgement is embraced over the adherence to rigid protocols. Wherever possible, I have tried to introduce new concepts with concrete examples that will likely be familiar to the reader and therefore serve as a useful starting point for understanding the more abstract concepts. After reading this book, the reader should be able to understand and communicate fundamental concepts of introductory statistics and apply them in the biological and environmental sciences.

This book originated as a workbook for the second-year university statistics class that I teach for biological and environmental science students at the University of Stirling in Stirling, Scotland. I wanted to rebuild the learning content from scratch after switching from proprietary statistical software to the free and open-source jamovi . There are a lot of good statistics textbooks (many of which are cited throughout this book), but I could not find anything for biological or environmental science students that used jamovi. As a teacher, I have found jamovi to be the ideal tool for teaching introductory statistics. Its user friendly interface has made it possible to focus on teaching statistical concepts rather than the details of navigating software. It works on Windows, Mac, Linux, or Chrome and can even be used in a browser (https://www.jamovi.org/), so I know that my students will have access to jamovi after they are done with my class. I hope that this book is helpful for teachers wanting to use jamovi in the classroom, and that it is useful for anyone who wants to understand and apply statistical techniques that are widely used across the biological and environmental sciences.

## How this book is structured

This book has 35 chapters, with 26 chapters introducing statistical concepts and 9 chapters focused on the practical skills required to apply them in jamovi. These chapters are interspersed throughout the book such that chapters introducing inter-related statistical concepts are followed by a practical skills chapter (the exception is a final chapter on randomisation, which is not followed by a practical skills chapter). Conceptual chapters are generally quite short, and longer chapters are broken down into manageable subsections. Practical skills chapters include multiple exercises in jamovi (except for Chapter 3, which uses spreadsheets outwith jamovi). Answers to exercise questions can be found in Appendix A. Together, a set of conceptual chapters and the practical skills chapter that follows (e.g., Chapters 1–3) could be used as one week of material in an introductory statistics class. In several places throughout the book, there are footnotes with links to interactive applications that I have written in shiny . These applications should make it easier to visualise some of the more challenging concepts and reinforce the text.

## Datasets used in this book

Datasets for all exercises (and code for shiny apps) are freely available to download from the Open Science Framework (https://osf.io/dxwyv). They are also stored on GitHub (https://github.com/bradduthie/stats), and can be accessed there using direct links in footnotes.

Datasets are mostly inspired by, or directly sourced from, real research in the biological and environmental sciences. A lot of data were collected from, or based on, my own doctoral work at Iowa State University. Other datasets are inspired by projects led by my colleagues at the University of Stirling. These data are for pedagogical purposes only. With some exceptions from my own data collected from the Sonoran Desert Rock Fig (Ficus petiolaris) system , all data in this book were constructed solely to illustrate statistical concepts and techniques as effectively as possible. Any data introduced in this book should therefore be treated as entirely hypothetical, and the reader will be reminded of this in subsequent chapters.

## Acknowledgements

Most of this book was written in the summer and autumn of 2022, and during a busy spring semester of teaching statistics in 2023. I am very grateful for the feedback that I have received from undergraduate students in the 2023 and 2024 Statistical Techniques class at the University of Stirling. I love teaching this class, and conversations that I have had with students during weekly practical sessions have made the text clearer and corrected numerous typographical errors. The book has also benefited from suggestions and corrections from numerous PhD students who served as teaching assistants on the class, including Adam Fell, Adrian Bach, Arianna Chiti, Benjamin Marshall, Chloe Pow, Daniel Atton Beckman, Eleri Kent, Rebecca Metcalf, and Shubham Pawar. I am especially grateful to my co-teachers Martina Quaggiotto and Ian Jones. Martina and Ian supported my plan to overhaul the learning material for our statistics class and put in a lot of work to fix teaching content that was not actually broken. I am also grateful to several colleagues at the University of Stirling who have provided images and inspired me with interesting projects and datasets, including Alan Law, Becky Boulton, Carmen Carmona, David Copplestone, Elisa Fuentes-Montemayor, Izzy Jones, Jens-Arne Subke, Jessica Burrows, Kirsty Park, Lidia de Sousa Teixeira, Matthew Tinsley, Nigel Willby, and Nils Bunnefeld.

I want to thank CRC Press, and especially Lara Spieker, for making the publishing process such a positive experience. This book has benefited from feedback from three anonymous reviewers. It was written using Bookdown within RStudio in the R programming language running on the Xubuntu open source operating system (https://xubuntu.org/).

I want to thank my kid, Emrys. They are a joy in my life and have taught me so much.

Lastly, I want to thank my wife Catherine. We have supported one another through so many challenges in life, through the uncertainties of career, immigration, and COVID-19. Her support has been indispensable for writing this book, and I will always be grateful for it.

A. Bradley Duthie (http://bradduthie.github.io) is a Lecturer in Environmental Modelling at the University of Stirling, Scotland, UK. He completed undergraduate degrees in Biology and Philosophy at Southern Illinois University Edwardsville. He earned his PhD in Ecology and Evolutionary Biology from Iowa State University with a graduate minor in Statistics. His research focuses primarily on theory and modelling in ecology and evolutionary biology with particular interests in evolutionary ecology and community ecology. He also contributes to several research projects as a biostatistician and wrote and maintains two R packages for agent-based modelling. As an educator, Brad completed a Graduate Student Teaching Certificate at Iowa State University and became a Fellow of the Higher Education Academy while working as a Postdoctoral Research Fellow at the University of Aberdeen. He has taught undergraduate classes in statistics, ecology, and evolution at the University of Stirling and is the Programme Director for Biology and Animal Biology.

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