Understanding Data
A 21st Century Approach to Statistics and Data Science
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Beschreibung
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.
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.
Simulation-based approach enables students/readers to experience Statistics directly Resampling-based approach gives a method to calculate critical concepts like confidence intervals without assumptions Frees scientific inquiry versus fitting thought processes into formulas
Autor*in
Alan Garfinkel
Themen in »Understanding Data«
Bayesian statistical approach kernel density estimates data visualization ANOVA statistical regression t-test Mt. Fuji plot NHST relative risk bivariate data X-Y scatterplot linear regression p-values