Andrew Gelman

Andrew Gelman is a professor of statistics and political science at Columbia University and one of the most influential statisticians in social science research. His work spans statistical methods, Bayesian statistics, hierarchical modeling, and applications in political science and social research. Gelman developed several key statistical methods and tools, including the widely-used Stan programming language for statistical modeling. His book "Bayesian Data Analysis" (co-authored with John Carlin, Hal Stern, and Donald Rubin) is considered a fundamental text in the field of Bayesian statistics. He maintains a popular blog on statistics and social science methodology and has been vocal about issues of research reproducibility and statistical practice. His critiques of p-values and advocacy for more rigorous statistical methods have influenced how social scientists approach quantitative research. Gelman has received numerous awards including the DeGroot Prize and the American Statistical Association's award for outstanding statistical application. His research has been cited tens of thousands of times, and his methods are used across disciplines including political science, psychology, and public health.

Readers praise Gelman's clear explanations of complex statistical concepts and his ability to connect theory to real-world applications. His book "Bayesian Data Analysis" receives consistent praise for its comprehensive coverage and practical examples, though some readers note it requires solid mathematical foundation. Liked: - Direct writing style that avoids unnecessary jargon - Practical examples that illustrate theoretical concepts - Blog posts that make statistics accessible - Focus on research integrity and methodology Disliked: - Technical density makes some works inaccessible to beginners - Math prerequisites not always clearly stated - Some find his blog critiques of other researchers too harsh Ratings: - Bayesian Data Analysis: 4.5/5 on Goodreads (300+ ratings) - Data Analysis Using Regression: 4.3/5 on Amazon (80+ reviews) One reader noted: "His explanations clicked when other texts failed." Another commented: "The math got overwhelming without more foundational knowledge." Blog readers appreciate his "no-nonsense approach to calling out poor statistical practice" while some find his tone "unnecessarily confrontational."

Bayesian Data Analysis (1995, with later editions) A comprehensive textbook covering Bayesian statistical methods, computational techniques, and hierarchical modeling.

Teaching Statistics: A Bag of Tricks (2002, with Deborah Nolan) A collection of classroom demonstrations and activities for teaching statistical concepts to students.

Data Analysis Using Regression and Multilevel/Hierarchical Models (2006, with Jennifer Hill) A practical guide to regression analysis and hierarchical models using examples from social sciences and public health.

Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (2008) An analysis of voting patterns in U.S. presidential elections examining the relationship between income and political preferences.

A Quantitative Tour of the Social Sciences (2009, edited with Jeronimo Cortina) A series of essays exploring how quantitative methods are applied across different social science disciplines.

Regression and Other Stories (2020, with Jennifer Hill and Aki Vehtari) A textbook on regression analysis covering fundamental concepts, model building, and causal inference.

Nate Silver Focuses on statistical analysis of politics, sports, and forecasting through his FiveThirtyEight platform. His approach to Bayesian reasoning and data-driven analysis aligns with Gelman's statistical philosophy.

John Kruschke Writes about Bayesian statistics and cognitive science with an emphasis on practical applications. His work bridges academic statistical concepts with real-world problem solving.

Edward Tufte Explores the visual display of quantitative information and data presentation principles. His work on graphical excellence complements Gelman's focus on statistical communication and visualization.

David MacKay Combines information theory with statistical inference and machine learning fundamentals. His treatment of probability and Bayesian methods shares common ground with Gelman's technical frameworks.

Philip Tetlock Studies forecasting, decision making, and expert political judgment through empirical research. His work on prediction accuracy and systematic thinking parallels Gelman's interests in political science and methodology.