Statistics for the Biosciences and Bioengineering

A Practical Guide to Data Analysis

Author

William Cresko

Published

January 2026

This books is meant to accompany the graduate statistics courses I teach at the University of Oregon, including Statistics for Bioengineering. This book provides the foundational skills needed for a successful scientific career in bioengineering and biosciences, combining rigorous statistical theory with practical computational implementation in R.

0.1 Why This Book?

Modern biosciences and bioengineering research generates vast amounts of data—from RNA sequencing experiments to biomaterial characterization studies, from neural recordings to clinical trial outcomes. Making sense of this data requires more than just running statistical tests; it demands a deep understanding of the principles underlying those tests and the computational skills to implement them properly.

This course takes a practical, hands-on approach to learning statistics. Rather than deriving every formula from first principles, we will focus on understanding when and why to apply particular methods, how to implement them in R, and how to interpret and communicate results. Throughout the book, you’ll work with real biological data and develop the skills to analyze your own research questions.

0.2 What You Will Learn

The material spans several interconnected domains:

Computational Foundations. You will develop proficiency in basic Unix and R programming, including the tidyverse ecosystem for data manipulation and visualization. These tools form the backbone of modern reproducible research practices.

Statistical Theory. The book covers probability distributions, parameter estimation, hypothesis testing, and the logic of statistical inference. Understanding these concepts allows you to choose appropriate methods and interpret results correctly.

Practical Analysis Methods. From t-tests to linear regression to analysis of variance, you’ll learn to implement a wide range of statistical techniques. Each method is presented with clear guidance on assumptions, implementation in R, and interpretation of output.

Reproducible Research. Using Markdown, Git, and GitHub, you’ll learn to document your analyses in ways that others (including your future self) can understand and reproduce.

0.3 Additional Resources

0.4 Software Requirements

0.5 How to Use This Book

Each chapter builds on previous material, so working through the book sequentially is recommended for beginners. However, the modular organization also allows readers with some background to jump to specific topics of interest.

Code examples are provided throughout the text, and you are strongly encouraged to type them yourself rather than copying and pasting. The act of typing reinforces learning and helps you notice details that might otherwise slip by. When you encounter errors—and you will—treat them as learning opportunities.

The exercises at the end of each chapter progress from straightforward applications of the material to more challenging problems requiring synthesis across topics. Attempting these exercises, even when difficult, is essential for developing genuine competence.

0.6 Acknowledgments

This book grew out of many years of teaching biostatistics at the University of Oregon. I am grateful to the many students whose questions and struggles have shaped how I present this material, and to colleagues who have shared their insights on effective teaching of statistics. In particular Clay Small, Andrew Muehlheisen, Hannah Tavalire, Peter Ralph, Sabrina Moustofi and Hope Healey helped create previous versions of this material.

Bill Cresko Institute of Ecology and Evolution Knight Campus for Accelerating Scientific Impact
University of Oregon