Making Statistics Work: Information Theory and Bayesian Inference

About the Book

Conventional "frequentist" methods that dominate the field of statistics are generally inconsistent and liable to catastrophic failure in some contexts. These weaknesses have become particularly concerning in relation to crises of replicability and credibility in science. Two alternatives have been proposed to address these flaws--classical Bayesian inference and the principle of maximum entropy--but the connections between them remain controversial.

Making Statistics Work presents a synthesis of information theory and Bayesian inference that addresses these fundamental problems. It provides a consistent, powerful, and flexible framework for data inference based on rigorous logic derived from first principles, allowing for new approaches to many of the unresolved questions of statistics. Duncan K. Foley and Ellis Scharfenaker illustrate the application of this framework and the reasoning behind it across a variety of important statistical problems, such as the inference underlying "gold standard" clinical trials, models of human behavior employed in behavioral finance and psychology, analysis of macroeconomic policy, the relationship of classical probability to quantum physics, and the limitations of linear regression analysis. Making Statistics Work offers new insight into contentious topics, from problems of causality and confounding variables in randomized experimental trials to the foundations of Bayesian and frequentist probability theory.

About the Author :

Duncan K. Foley is the Leo Model Professor Emeritus of Economics at the New School for Social Research. He is the author of Understanding Capital: Marx's Economic Theory (1986) and Adam's Fallacy: A Guide to Economic Theology (2006) and coauthor of Growth and Distribution (second edition, 2019), among other books.

Ellis Scharfenaker is an associate professor of economics at the University of Utah. His research integrates Bayesian inference, information theory, and political economy to study industrial dynamics and income distribution.

Review :
In Making Statistics Work, Duncan K. Foley and Ellis Scharfenaker combine information theory, Bayesian updating, and probability theory into a single logical framework for statistical inference under imperfect and insufficient information. The authors provide many examples, making the book very accessible. This is a valuable resource for scientists, students, and teachers across disciplines.--Amos Golan, American University and the Santa Fe Institute At last, a statistics book that engages the philosophical foundations of probability and fully develops their implications through to practice. Clear, rigorous, and refreshingly honest about assumptions, it sets a new standard and should be required reading for anyone serious about statistics.--Aubrey Clayton, author of Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science Making Statistics Work introduces a robust framework that brings together information theory and Bayesian inference through entropy-maximizing priors. Offering both readability and rigor, this book is a refreshing alternative to the conventional statistical education.--Jangho Yang, University of Waterloo