The New Statistics with R: An Introduction for Biologists by Andy Hector – A Modern Guide to Statistical Analysis for Li

Introduction

Statistical analysis forms the backbone of biological research, yet mastering it can be a daunting task for students and early-career scientists. The New Statistics with R: An Introduction for Biologists, written by Andy Hector and published by Oxford University Press, offers a clear, modern, and practical guide to using R for statistical modelling. This hardcover edition is an essential resource for Indian biology students, researchers, and professionals who want to move beyond traditional p-value-driven methods and embrace a more robust, estimation-based approach. Whether you are analysing field data, lab experiments, or ecological surveys, this book equips you with the tools to interpret data confidently and communicate findings effectively.

Book Overview

This updated edition builds on a decade of classroom testing to deliver a proven textbook that teaches linear and generalized linear models with R. It covers classical techniques such as t-tests, regression, and ANOVA, while also introducing modern extensions like information criteria and estimation-based inference. The book is designed for biologists and environmental scientists who need a hands-on, example-driven approach. With a focus on real-world biological data, it bridges the gap between theory and practice, making statistical concepts accessible without sacrificing depth.

Key Highlights

• Estimation-based approach that emphasizes effect sizes and confidence intervals over p-values alone
• Thoroughly road-tested content refined over a decade of teaching
• Latest R advances included, with code and datasets for all examples
• Classical and modern methods combined: t-tests, regression, ANOVA, and generalized linear models
• Critique of over-reliance on p-values and introduction of information criteria for model selection

Inside the Book

The book is structured to guide readers from basic concepts to advanced modelling. Each chapter begins with clear learning objectives and uses biological examples—from plant growth to animal behaviour—to illustrate key ideas. R code is integrated throughout, and every analysis is explained step by step. The text also includes exercises at the end of chapters to reinforce learning. Special attention is given to data visualization, model checking, and interpretation of results, ensuring that readers not only run analyses but also understand what the numbers mean in a biological context.

Key Topics

• Introduction to R and data handling for biologists
• Linear models: t-tests, ANOVA, and regression
• Model assumptions and diagnostics
• Generalized linear models (GLMs) for count and binary data
• Model selection using AIC and information criteria
• Estimation and effect sizes
• Mixed-effects models for nested and repeated measures
• Graphical presentation of statistical results

Reader Benefits

By working through this book, readers gain a solid foundation in modern statistical thinking. The estimation-based approach helps avoid common pitfalls of null-hypothesis testing, making your research more reproducible and credible. You will learn to write efficient R code, create publication-quality graphs, and select appropriate models for your data. The practical exercises and real datasets ensure that you can immediately apply what you learn to your own research projects. For Indian students preparing for competitive exams or postgraduate research, this book provides a competitive edge in quantitative biology.

Learning Outcomes

• Confidently use R to import, clean, and visualize biological data
• Perform and interpret linear and generalized linear models
• Critically evaluate p-values and adopt estimation-based inference
• Select the best statistical model using information criteria
• Present statistical results clearly in reports and publications
• Troubleshoot common modelling issues with diagnostics

Who Should Read

This book is ideal for undergraduate and postgraduate students in biology, ecology, environmental science, agriculture, and related fields. It is also valuable for researchers and professionals who want to update their statistical skills with modern R-based methods. No prior experience with R is required, though basic familiarity with biology and mathematics will be helpful. Indian instructors teaching statistics courses in life sciences will find this a perfect textbook for their curriculum.

About the Author

Andy Hector is a Professor of Ecology at the University of Oxford, where he has taught statistics to biologists for many years. His research focuses on biodiversity and ecosystem functioning, and he has published extensively in top scientific journals. His hands-on teaching experience and deep understanding of both statistics and biology make him an ideal guide for learners navigating the world of data analysis.

About the Publisher

Oxford University Press is a globally respected academic publisher known for its high-quality textbooks and reference works. With a strong commitment to educational excellence, OUP ensures that this book meets rigorous standards of accuracy, clarity, and pedagogical value. Their publications are widely used in Indian universities and research institutions.

Conclusion

The New Statistics with R: An Introduction for Biologists is more than just a textbook—it is a practical companion for anyone who wants to analyse biological data with confidence and insight. By combining classical methods with modern developments, Andy Hector has created a resource that prepares readers for the realities of contemporary research. Order your hardcover copy from Bookshops.in today and take a decisive step toward mastering statistics in biology.