Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not: Future Prediction Using Probability and Statistical Inference continues to offer an informative guide to probability and prediction. The expanded second edition contains problem and solution sets, providing readers with practical tools to apply these concepts. In addition, the book’s illustrative examples reveal how we are living in a statistical world, what we can expect, what we really know based upon the information at hand, and explains when we only think we know something.

The author introduces the principles of probability and explains probability distribution functions. The book covers combined and conditional probabilities and features a new section on Bayes’ Theorem and Bayesian Statistics, which includes simple examples such as the Presecutor’s Paradox, and explores Bayesian vs. Frequentist thinking about statistics. A chapter dedicated to Benford’s Law is also included, exploring its use in measuring compliance and financial fraud detection.

With relevant mathematics and examples that demonstrate how to use the concepts presented, this book serves as an accessible introduction to the fascinating world of probability and statistical inference for both students and enthusiasts alike.