Acknowledgments

This textbook was made possible by the collective wisdom of statisticians, educators, and learning scientists whose work has shaped how we teach and learn statistics.

Intellectual Foundations

The pedagogical approach in this book draws on decades of research in the learning sciences. We are particularly indebted to:

  • Robert Bjork and Elizabeth Bjork for their research on desirable difficulties, spacing effects, and interleaving — principles embedded in every chapter of this book
  • Henry Roediger and Mark McDaniel for their work on retrieval practice, which informs our "Check Your Understanding" prompts
  • George Cobb for his influential argument that introductory statistics should emphasize concepts and data analysis over formulas and procedures — a philosophy that permeates this text
  • The GAISE (Guidelines for Assessment and Instruction in Statistics Education) report authors for establishing a framework for modern statistics education that emphasizes statistical thinking, real data, and conceptual understanding

Textbook Influences

We gratefully acknowledge the textbooks that established the standard for introductory statistics instruction:

  • Mario F. Triola, Elementary Statistics — the most widely adopted introductory textbook, whose topic sequence and applied emphasis influenced our chapter organization
  • David Moore, The Basic Practice of Statistics — whose conversational tone and emphasis on data production inspired our approach
  • OpenStax, Introductory Statistics — a pioneering open educational resource that demonstrated that free textbooks can be excellent textbooks
  • Charles Wheelan, Naked Statistics — whose proof that statistics writing can be genuinely entertaining set a high bar for accessibility
  • Allen Downey, Think Stats — whose computational-first approach showed how programming makes statistics more intuitive

Data and Examples

The examples and case studies throughout this book draw on publicly available datasets from:

  • The Centers for Disease Control and Prevention (CDC)
  • The World Health Organization (WHO)
  • The U.S. Census Bureau
  • The Bureau of Labor Statistics
  • Gapminder
  • The National Oceanic and Atmospheric Administration (NOAA)
  • The FBI Uniform Crime Reporting Program
  • ESPN and Basketball Reference
  • Various published research studies (cited within chapters)

Technology

The Python examples in this book rely on the excellent open-source scientific computing ecosystem, particularly: NumPy, pandas, matplotlib, seaborn, and SciPy — maintained by thousands of contributors worldwide.

Open Educational Resources Community

This textbook is released as an open educational resource (OER) because we believe that financial barriers to quality education are both unnecessary and unjust. We are grateful to the broader OER community for demonstrating that open access and high quality are not mutually exclusive.

Readers and Contributors

Finally, we thank the students and instructors who use this book. Your feedback makes it better. If you find an error, a confusing explanation, or a missed opportunity, please contribute — this book improves with every reader's input.