Read Calling Bullshit: The Art of Skepticism in a Data-Driven World By Carl T. Bergstrom,Jevin D. West

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Calling Bullshit: The Art of Skepticism in a Data-Driven World-Carl T. Bergstrom,Jevin D. West

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Bullshit isn’t what it used to be. Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data.   “A modern classic . . . a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic.”—WiredMisinformation, disinformation, and fake news abound and it’s increasingly difficult to know what’s true. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don’t feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data. You don’t need a lot of technical expertise to call out problems with data. Are the numbers or results too good or too dramatic to be true? Is the claim comparing like with like? Is it confirming your personal bias? Drawing on a deep well of expertise in statistics and computational biology, Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization, distinguish between correlation and causation, and examine the susceptibility of science to modern bullshit. We have always needed people who call bullshit when necessary, whether within a circle of friends, a community of scholars, or the citizenry of a nation. Now that bullshit has evolved, we need to relearn the art of skepticism.

Book Calling Bullshit: The Art of Skepticism in a Data-Driven World Review :



This book provides timely information. The many processes and concepts ripe for exploitation by bullshit artists are often those that are not intuitive, or readily identifiable by the incognoscenti.One major flaw is the author's unfortunate choice not to number their references, and instead provide readers with an alphabetized list at the end wherein one is forced to make a best guess at matching the correct referenced material behind the statements made within the text. This choice of format soon gets rather annoying and is somewhat ironic, given the title of the book, and the emphasis the authors repeatedly place on proper fact checking.Note to the authors: Within the preface section, the letter "M" used within the acronym STEM, as per Charles Vela, and subsequently the NSF, is intended to refer to Mathematics, not Medicine.
Brandolini’s law, which states that “the amount of energy needed to refute BS is an order of magnitude bigger than that needed to produce it,” explains why there is so much BS in the world. As Uriel Fanelli put it, “an idiot can create more BS than you could ever hope to refute.”So creating BS is easy; refuting it is hard. And it is precisely this asymmetry that explains why BS persists and how it can even grow over time.So how can one hope to rid the world of increasing levels of BS? Since it’s easier to create BS than to refute it, simply refuting each new instance of BS seems like a losing battle. The better strategy is educational; if you can inoculate enough people against falling for BS in the first place, BS never gains enough traction to require costly efforts at refutation.This, in essence, is the goal of the book. The authors want to immunize you against BS, with a focus on the quantitative variety. While it’s relatively easy to identify old-school BS based on flowery language and empty rhetoric, new-school BS is more insidious and sophisticated with its use of statistics, charts, graphs, and scientific-sounding claims. This is the BS that is more persuasive, harder to refute, and ultimately more dangerous.The authors first note that while arguments based on statistical and scientific reasoning can appear intimidating, there are basic fallacies that one can look out for that do not require any advanced mathematical ability. It is rarely necessary to look into the “black box”—the authors’ term for complex equations, algorithms, or scientific processes—when the problem with BS is often the data that feeds into the black box. Recognizing that the data is biased or unrepresentative of the larger population, for example, is an easy method of spotting BS that does not require any skills in higher mathematics.The authors then take the reader on a tour of quantitative fallacies with several examples, all explained clearly and with humor. The reader will learn how to differentiate between correlation and causation, spot biased and unrepresentative data and small sample sizes, identify selection biases in samples, understand how data can be manipulated visually, and more. The reader will also learn how to properly evaluate scientific claims, and how the anti-vaxx movement is based on a single, thoroughly-debunked scientific study that massively confuses correlation with causation, among many other problems.One of my favorite chapters, chapter 8, has the authors calling BS on arguments that claim that artificial intelligence will take over the world. This has always been BS and likely always will be, as the authors demonstrate the limits of how machines are designed to “think.”The book ends with a couple summary chapters on how to spot and refute BS, and also on the difference between calling legitimate BS and becoming what the authors refer to as a “well-actually guy.” Perhaps the most important point of the book is the idea that the goal of calling BS is not to demonstrate your intelligence and puff up your ego; it’s to counter the spread of misinformation in the world and its direct and indirect consequences.Overall, I suppose that if the reader has a lot of experience with informal logic and spotting fallacies—particularly of a quantitative nature—then this book might not offer anything particularly new. Although even then the book is filled with interesting, updated examples and a ton of polemical humor which makes the book a fun read. If, on the other hand, the reader has limited experience with these concepts, this book is a must read as it shows how quantitative BS can be spotted and refuted with even the most limited of mathematical ability.

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