The New Statistics with R: An Introduction for Biologists
Md. Moyazzem Hossain · Journal of the Royal Statistical Society Series A (Statistics in Society) · 2025
Nowadays, Statistics play a vital role in making effective and efficient decisions across all study domains. Data analysis skills are crucial for making data-driven decisions, and R/RStudio programming is an excellent way to improve researchers’ data management and analysis skills in any area of study. This book consists of 20 chapters. Introduction, motivation, and description are presented in Chapters 1–3. The authors nicely describe the reproducibility crisis and provide a short example of a script, and then convert it to an R Markdown document in Chapter 4, titled Reproducible Research. Chapters 5–10 cover the ideas of estimation, confidence intervals, hypothesis testing, and predictions. Chapter 7 discusses correlation and regression with examples. In the following chapters, this book moves from linear models (ANOVA, regression, and ANCOVA) to generalized linear models (GLMs) (for count data, binary, etc.). The authors present a very short introduction in the final chapter. Additionally, the worked examples are accompanied by supporting materials (data sets and R scripts). The book's concise, well-annotated R code examples are a major plus for beginning users, especially those who have no prior programming knowledge. Each chapter integrates conceptual explanations with practical R code and biological examples drawn from ecology, environmental science, and evolution. However, readers entirely new to statistical concepts may find the transition from descriptive statistics to GLMs challenging without supplementary reading and will need to look elsewhere. Adding exercise in each chapter will be helpful for the readers. The book is ideally suited for upper-level undergraduate and postgraduate students in biology, ecology, and environmental science, as well as for early-career researchers who wish to strengthen their quantitative skills. As a result, I suggest it to academics, data scientists, and students who study the biological sciences, where making precise conclusions is important. Not applicable.