Book Review: Introduction to Modern Statistics (2e) (Free PDF)

Statistics has rapidly evolved in recent years, driven by the data revolution. Whether you're a data enthusiast, a student in STEM, or a professional trying to sharpen your analytical skills, having a strong foundation in modern statistical thinking is essential. That’s exactly what Introduction to Modern Statistics (2e) offers — a fresh, data-first approach to statistics that reflects how the subject is practiced today.

What Is This Book About?

Introduction to Modern Statistics (2e) is an open-access textbook written by Mine Çetinkaya-Rundel and Johanna Hardin, both of whom are respected statisticians and educators. The book takes a modern, computational, and conceptual approach to teaching statistics — rooted in real-world datasets and R-based workflows.

Unlike traditional textbooks that focus heavily on mathematical derivation, this book emphasizes:

• Data exploration

• Visualization

• Inference using simulation

• Modern data practices

• Computational reproducibility

Key Features

1. Open-Source and Freely Available

The entire book is available under a Creative Commons license — meaning it’s completely free to read, modify, and distribute. This makes it a valuable resource for schools, educators, and self-learners.

2. Real-World Data Examples

Throughout the book, the authors use real, messy datasets rather than toy examples. This helps bridge the gap between statistical theory and real-life data analysis.

3. Code-Based Learning with R

Each chapter includes R code examples and instructions, promoting hands-on experience with:

• ggplot2 for visualization

• dplyr for data manipulation

• infer for inference via simulation

• tidyverse as the foundational grammar

4. Clear Conceptual Explanations

The book does a great job of explaining complex ideas in a simple, digestible way — using visuals, examples, and step-by-step logic.

5. Flexible for Instructors

Each section comes with instructor resources, slides, labs, and exercises, making it ideal for course adoption in universities and online programs.

Topics Covered

• Data visualization and summarization

• Sampling and study design

• Probability and distributions

• Bootstrapping and simulation

• Inference for proportions and means

• Regression modeling

• Introduction to Bayesian statistics

Who Should Read This Book?

• Undergraduate students in statistics, data science, psychology, economics, or life sciences

• High school AP Statistics learners

• Educators seeking a fresh and inclusive approach

• Self-learners and R users looking to brush up on statistics

• Data professionals needing a strong foundation in statistical thinking

Pros

• Free and open-access

• Focus on modern practices (tidy data, reproducibility)

• Includes interactive R tutorials and labs

• Strong emphasis on conceptual understanding

• Written by two experienced educators

• Updated to align with current best practices

Cons

• Requires basic familiarity with R (although gradual)

• May not suit those looking for a deeply mathematical or calculus-based approach

Final Thoughts

Introduction to Modern Statistics (2e) is more than just a statistics textbook — it's a manifesto for how statistics should be taught in the 21st century. It’s approachable, modern, inclusive, and practical.

If you're looking to learn statistics not just as a subject, but as a skill—backed by real-world data and reproducible code—this book is a must-read.

PDF: Link: Introduction to Modern Statistics (2e)

Hard Copy: Introduction to Modern Statistics (2e)