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A Field Guide to Lies: Critical Thinking in the Information Age Hardcover – September 6, 2016
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Winner of the 2016 Mavis Gallant Prize for Nonfiction
One of Hudson Booksellers' Best Business Interest Books of 2016
“Daniel Levitin’s field guide is a critical thinking primer for our shrill, data-drenched age. It’s an essential tool for really understanding the texts, posts, tweets, magazines, newspapers, podcasts, op-eds, interviews and speeches that bombard us every day. From the way averages befuddle to the logical fallacies that sneak by us, every page is enlightening.”—Charles Duhigg, author of The Power of Habit and Smarter, Faster, Better
“The world is awash with data, but not always with accurate information. A Field Guide to Lies does a terrific job of illustrating the difference between the two with precision—and delightful good humor.”—Charles Wheelan, Senior Lecturer and Policy Fellow, Rockefeller Center, Dartmouth College, author of Naked Economics
“A Field Guide to Lies by the neuroscientist Daniel Levitin lays out the many ways in which each of us can be fooled and misled by numbers and logic, as well as the modes of critical thinking we will need to overcome this.”—The Wall Street Journal
“Valuable tools for anyone willing to evaluate claims and get to the truth of the matter.”—Kirkus Reviews
“This useful, entertaining, and highly readable guide is ready to arm everyday citizens with the tools to combat the spread of spurious, and often ridiculous, information.”—Library Journal
“[A] book you may want to have close by at all times.”—Success Magazine
“[A Field Guide to Lies] serves as a kind of Strunk & White for sloppy thinkers.”—New York Journal of Books
“Entertaining and filled with helpful hints, A Field Guide to Lies is a valuable tool for navigating the daily data onslaught.”—San Jose Mercury News
“[S]mart and humorous…the tools anyone needs to tell good information from bad are in this definitive guide to critical thinking.”—Shelf Awareness
“Exceptional ... practical and essential advice.”—Big Think
“An entertaining, user-friendly primer on evaluating data wisely.”—The Washington Independent Review of Books
“This is a wonderful book. It covers so many of the insights of science, logic, and statistics that the public needs to know, yet are sadly neglected in the education that most of us receive.”—Edward K. Cheng, Tarkington Chair of Teaching Excellence and professor of law at Vanderbilt Law School
“Hits on the most important issues around statistical literacy, and uses good examples to illustrate its points. I could not put this book down. Reading it has been a pleasure, believe me. I am so impressed with Levitin's writing style, which is clear and simple, unlike much of the murky stuff that is written by statisticians and many others.”—Morris Olitsky, former Vice President, Market Research and Analysis, Prudential Financial, Statistician, U.S.D.A.
“Insightful and entertaining—an excellent work.”—Gregg Gascon, Biomedical Informatics, Ohio State University
“Just as Strunk and White taught us how to communicate better, the Field Guide to Lies is an indispensable guide to thinking better. As Big Data becomes a dominant theme in our culture, we are all obliged to sharpen our critical thinking so as to thwart the forces of obfuscation. Levitin has done a great service here.”—Jasper Rine, Professor of Genetics, Genomics, and Development, UC Berkeley
“Not since Huff's classic How to Lie with Statistics has a book so clearly described how numbers can be used to deceive and misdirect. Levitin shows how to critically evaluate claims that charlatans, the media, and politicians would have us believe.”—Stan Lazic, Team Leader in Quantitative Biology at AstraZeneca
“A must read! Professor Levitin convinces the reader why critical thinking has become even more crucial in the Information Age. As we are consistently bombarded with information, let’s question its veracity and acquire the tools to analyze it.”—Isabelle Bajeux-Besnainou, Dean and Professor of Finance, Desautels Faculty of Management , McGill University
“Well researched, and provides a valuable guide to assist the public with a methodology for evaluating the truth behind this cacophony of information that constantly inundates.”—Patrick Martin, Magician
“[A] valuable primer on critical thinking that convincingly illustrates the prevalence of misinformation in everyday life.”—Publisher's Weekly
About the Author
Daniel J. Levitin, Ph.D., is Founding Dean of Arts & Humanities at the Minerva Schools at KGI, a Distinguished Faculty Fellow at the Haas School of Business, UC Berkeley, and the James McGill Professor Emeritus of Psychology and Music at McGill University, Montreal, where he also holds appointments in the Program in Behavioural Neuroscience, The School of Computer Science, and the Faculty of Education. An award-winning scientist and teacher, he now adds best-selling author to his list of accomplishments as This Is Your Brain on Music , The World in Six Songs and The Organized Mind were #1 best-sellers. His work has been translated into 21 languages. Before becoming a neuroscientist, he worked as a session musician, sound engineer, and record producer working with artists such as Stevie Wonder and Blue Oyster Cult. He has published extensively in scientific journals as well as music magazines such as Grammy and Billboard. Recent musical performances include playing guitar and saxophone with Sting, Bobby McFerrin, Rosanne Cash, David Byrne, Cris Williamson, Victor Wooten, and Rodney Crowell.
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I really liked the author splitting his subject in three main categories: 1) evaluating numbers; 2) evaluating words; 3) how the scientific method works. Each section covers its respective subject in a thorough and entertaining way.
Within the evaluating numbers section the author covers all the ways in which visual quantitative data (graphs) can fool you. You really have to watch very carefully the scale of the X- and Y-axes to understand if someone is trying to trick you. The author does a good job of explaining the difference between correlation and causation (and how not to confuse the two). He also warns you not to confuse what is tested as statistically significant and yet can be immaterial (small differences pop up as statistically significant that have little bearing on the outcome when you have very large samples). The author also warns against extrapolating trends especially when you go outside the boundaries of the variable values you observed within the learning sample of your data set. The author covers well the various biases and errors that can affect sampling (participation bias, reporting bias, etc.). The chapter on probabilities is excellent with a well-defined differentiation between classic probabilities, frequentist probabilities, and Bayesian probabilities.
Within the evaluating words section, the author warns about understanding the actual domain narrowness of experts. He does a good job of explaining the difference between incidence and prevalence rate. He provides a very good coverage on behavioral risk perception that is so detached from probabilistic thinking.
Within the scientific method section, the author defines the different types of reasoning (deduction, induction, abduction). He also covers logic and logic notation. He also covers in greater detail Bayesian statistics. The latter is a subject that permeates every section of the book. And, he does a good job of explaining Bayes thanks to his four- quadrant framework that is really helpful in calculating the related Bayesian statistics.
The author makes just one small error where he confuses R Square with R (correlation). R Square explains how much the variance in one variable can be explained by the other variable. Meanwhile, R simply tells you the strength and the direction of the relationship between those two variables. Also, remember R is often negative (so the explanation bit here not only is wrong but is divergent) meanwhile R Square can’t be negative by definition. This is a minor typo. I know the author knows that stuff. One math typo in a 250-page book is far better than most books on the subject.