Understanding Data: A 21st Century Approach to Statistics and Data Science

This text introduces statistics to beginning students in a distinctly original and non-traditional way. It assumes minimal mathematical or statistical background, yet offers substantial depth that will also engage experienced practitioners. Motivated by the growing call to move beyond the statistical practices and concepts that contributed to the current “reproducibility crisis,” the book encourages readers to rethink what statistics is, how it is used, and how it should be taught. Instead of memorizing formulas that were derived as approximations under unrealistic assumptions, modern computing enables us to simulate scenarios thousands of times in seconds and simply count outcomes.

Taking this computational approach as fundamental, the book provides thorough coverage of the material, including describing and presenting data, two-group and multi-group comparisons, correlation, regression, statistical power and Bayesianism, deliberately forgoing many standard techniques in favor of simulation-based methods. This philosophy is gaining momentum. The authors’ previous text, Modeling Life, which offers an equally distinctive approach to teaching calculus, continues to expand its readership and adoptions, and was awarded the Textbook and Academic Authors Association Textbook Excellence Award.

The writing captures the spirit of an engaging lecture―refreshingly candid, clearly explained, and richly illustrated. The text is filled with worked examples using real data, computer code, and practical guidance for presenting results effectively. Diagrams and figures enhance readability, and the resampling illustrations in particular provide intuitive visual cues that clarify the underlying processes. By revisiting key strategies throughout, the book builds conceptual understanding in layers, helping students develop a strong and coherent framework for success. The authors also provide templates for writing statistical methods sections appropriate for research papers and grant proposals. The book provides rich historical material tracing the relationship between statistics and its practical applications, giving important contexts for understanding how the dominant traditions in statistics came to be. Finally, it charts a pathway for the next generation of applied statistics students.

Alan Garfinkel received his undergraduate degree from Cornell in Mathematics and Philosophy and a PhD from Harvard in Philosophy and Mathematics. After several years of practicing philosophy of science, he transitioned to medical research, applying qualitative dynamics to studying oscillatory and other nonlinear phenomena in physiology and medicine. His contributions to teaching were recognized with the University Distinguished Teaching Award from the University of California, Los Angeles.

Yina Guo received her PhD in Control Engineering from Nankai University. Her PhD thesis used Partial Differential Equations to explain the branching structure of the lung. Her computer simulations of branching processes were featured on the cover of the Journal of Physiology. She is particularly interested in the use of graphics and visualization techniques in both research and teaching.