A Field Guide to Lies Read online

  Also By Daniel J. Levitin

  This Is Your Brain on Music: The Science of a Human Obsession

  The World in Six Songs: How the Musical Brain Created Human Nature

  The Organized Mind: Thinking Straight in the Age of Information Overload

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  Copyright © 2016 by Daniel J. Levitin

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  eBook ISBN: 9780698409798

  LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

  Names: Levitin, Daniel J., author.

  Title: A field guide to lies : critical thinking in the information age / Daniel J. Levitin.

  Description: New York : Dutton, [2016] | Includes bibliographical references and index.

  Identifiers: LCCN 2016007356 | ISBN 9780525955221 (hardcover) | ISBN 9781101985588 (export)

  Subjects: LCSH: Critical thinking. | Fallacies (Logic) | Reasoning.

  Classification: LCC BC177 .L486 2016 | DDC 153.4/2—dc23

  LC record available at http://lccn.loc.gov/2016007356

  While the author has made every effort to provide accurate telephone numbers, Internet addresses, and other contact information at the time of publication, neither the publisher nor the author assumes any responsibility for errors or for changes that occur after publication. Further, the publisher does not have any control over and does not assume any responsibility for author or third-party websites or their content.

  Version_1

  To Shari, whose inquisitive mind made me a better thinker

  CONTENTS

  Also By Daniel J. Levitin

  Title Page

  Copyright

  Dedication

  Introduction: Thinking, Critically

  PART ONE: EVALUATING NUMBERS

  Plausibility

  Fun with Averages

  Axis Shenanigans

  Hijinks with How Numbers Are Reported

  How Numbers Are Collected

  Probabilities

  PART TWO: EVALUATING WORDS

  How Do We Know?

  Identifying Expertise

  Overlooked, Undervalued Alternative Explanations

  Counterknowledge

  PART THREE: EVALUATING THE WORLD

  How Science Works

  Logical Fallacies

  Knowing What You Don’t Know

  Bayesian Thinking in Science and in Court

  Four Case Studies

  Conclusion: Discovering Your Own

  Appendix: Application of Bayes’s Rule

  Glossary

  Notes

  Acknowledgments

  Index

  About the Author

  INTRODUCTION

  THINKING, CRITICALLY

  This is a book about how to spot problems with the facts you encounter, problems that may lead you to draw the wrong conclusions. Sometimes the people giving you the facts are hoping you’ll draw the wrong conclusion; sometimes they don’t know the difference themselves. Today, information is available nearly instantaneously, but it is becoming increasingly hard to tell what’s true and what’s not, to sift through the various claims we hear and to recognize when they contain misinformation, pseudo-facts, distortions, and outright lies.

  There are many ways that we can be led astray by fast-talking, loose-writing purveyors of information. Here, I’ve grouped them into two categories, and they make the first two parts of this book: numerical and verbal. The first includes mishandled statistics and graphs; the second includes faulty arguments. In both parts, I include the steps we can take to better evaluate news, statements, and reports. The last part of the book addresses what underlies our ability to determine if something is true or false: the scientific method. It grapples with the limits of what we can and cannot know, including what we know right now and don’t know just yet, and includes some applications of logical thinking.

  It is easy to lie with statistics and graphs because few people take the time to look under the hood and see how they work. I aim to fix that. Recognizing faulty arguments can help you to evaluate whether a chain of reasoning leads to a valid conclusion or not. Related to this is infoliteracy—recognizing that there are hierarchies in source quality, that pseudo-facts can easily masquerade as facts, and biases can distort the information we are being asked to consider, leading us to faulty conclusions.

  You might object and say, “But it’s not my job to evaluate statistics critically. Newspapers, bloggers, the government, Wikipedia, etc., should be doing that for us.” Yes, they should, but they don’t always. We—each of us—need to think critically and carefully about the numbers and words we encounter if we want to be successful at work, at play, and in making the most of our lives. This means checking the numbers, the reasoning, and the sources for plausibility and rigor. It means examining them as best as we can before we repeat them or use them to form an opinion. We want to avoid the extremes of gullibly accepting every claim we encounter or cynically rejecting every one. Critical thinking doesn’t mean we disparage everything, it means that we try to distinguish between claims with evidence and those without.

  Sometimes the evidence consists of numbers and we have to ask, “Where did those numbers come from? How were they collected?” Sometimes the numbers are ridiculous, but it takes some reflection to see it. Sometimes claims seem reasonable, but come from a source that lacks credibility, like a person who reports having witnessed a crime but wasn’t actually there. This book can help you to avoid learning a whole lot of things that aren’t so. And catch some lying weasels in their tracks.

  We’ve created more human-made information in the last five years than in all of human history before them. Unfortunately, found alongside things that are true is an enormous number of things that are not, in websites, videos, books, and on social media. This is not just a new problem. Misinformation has been a fixture of human life for thousands of years, and was documented in biblical times and classical Greece. The unique problem we face today is that misinformation has proliferated; it is devilishly entwined on the Internet with real information, making the two difficult to separate. And misinformation is promiscuous—it consorts with people of all social and educational classes, and turns up in places you don’t expect it to. It propagates as one person passes it on to another and another, as Twitter, Facebook, Snapchat, and other social media grab hold of it and spread it around the world; the misinform
ation can take hold and become well known, and suddenly a whole lot of people are believing things that aren’t so.

  PART ONE

  EVALUATING NUMBERS

  It ain’t what you don’t know that gets you into trouble.

  It’s what you know for sure that just ain’t so.

  —MARK TWAIN

  PLAUSIBILITY

  Statistics, because they are numbers, appear to us to be cold, hard facts. It seems that they represent facts given to us by nature and it’s just a matter of finding them. But it’s important to remember that people gather statistics. People choose what to count, how to go about counting, which of the resulting numbers they will share with us, and which words they will use to describe and interpret those numbers. Statistics are not facts. They are interpretations. And your interpretation may be just as good as, or better than, that of the person reporting them to you.

  Sometimes, the numbers are simply wrong, and it’s often easiest to start out by conducting some quick plausibility checks. After that, even if the numbers pass plausibility, three kinds of errors can lead you to believe things that aren’t so: how the numbers were collected, how they were interpreted, and how they were presented graphically.

  In your head or on the back of an envelope you can quickly determine whether a claim is plausible (most of the time). Don’t just accept a claim at face value; work through it a bit.

  When conducting plausibility checks, we don’t care about the exact numbers. That might seem counterintuitive, but precision isn’t important here. We can use common sense to reckon a lot of these: If Bert tells you that a crystal wineglass fell off a table and hit a thick carpet without breaking, that seems plausible. If Ernie says it fell off the top of a forty-story building and hit the pavement without breaking, that’s not plausible. Your real-world knowledge, observations acquired over a lifetime, tells you so. Similarly, if someone says they are two hundred years old, or that they can consistently beat the roulette wheel in Vegas, or that they can run forty miles an hour, these are not plausible claims.

  What would you do with this claim?

  In the thirty-five years since marijuana laws stopped being enforced in California, the number of marijuana smokers has doubled every year.

  Plausible? Where do we start? Let’s assume there was only one marijuana smoker in California thirty-five years ago, a very conservative estimate (there were half a million marijuana arrests nationwide in 1982). Doubling that number every year for thirty-five years would yield more than 17 billion—larger than the population of the entire world. (Try it yourself and you’ll see that doubling every year for twenty-one years gets you to over a million: 1; 2; 4; 8; 16; 32; 64; 128; 256; 512; 1024; 2048; 4096; 8192; 16,384; 32,768; 65,536; 131,072; 262,144; 524,288; 1,048,576.) This claim isn’t just implausible, then, it’s impossible. Unfortunately, many people have trouble thinking clearly about numbers because they’re intimidated by them. But as you see, nothing here requires more than elementary school arithmetic and some reasonable assumptions.

  Here’s another. You’ve just taken on a position as a telemarketer, where agents telephone unsuspecting (and no doubt irritated) prospects. Your boss, trying to motivate you, claims:

  Our best salesperson made 1,000 sales a day.

  Is this plausible? Try dialing a phone number yourself—the fastest you can probably do it is five seconds. Allow another five seconds for the phone to ring. Now let’s assume that every call ends in a sale—clearly this isn’t realistic, but let’s give every advantage to this claim to see if it works out. Figure a minimum of ten seconds to make a pitch and have it accepted, then forty seconds to get the buyer’s credit card number and address. That’s one call per minute (5 + 5 + 10 + 40 = 60 seconds), or 60 sales in an hour, or 480 sales in a very hectic eight-hour workday with no breaks. The 1,000 just isn’t plausible, allowing even the most optimistic estimates.

  Some claims are more difficult to evaluate. Here’s a headline from Time magazine in 2013:

  More people have cell phones than toilets.

  What to do with this? We can consider the number of people in the developing world who lack plumbing and the observation that many people in prosperous countries have more than one cell phone. The claim seems plausible—that doesn’t mean we should accept it, just that we can’t reject it out of hand as being ridiculous; we’ll have to use other techniques to evaluate the claim, but it passes the plausibility test.

  Sometimes you can’t easily evaluate a claim without doing a bit of research on your own. Yes, newspapers and websites really ought to be doing this for you, but they don’t always, and that’s how runaway statistics take hold. A widely reported statistic some years ago was this:

  In the U.S., 150,000 girls and young women die of anorexia each year.

  Okay—let’s check its plausibility. We have to do some digging. According to the U.S. Centers for Disease Control, the annual number of deaths from all causes for girls and women between the ages of fifteen and twenty-four is about 8,500. Add in women from twenty-five to forty-four and you still only get 55,000. The anorexia deaths in one year cannot be three times the number of all deaths.

  In an article in Science, Louis Pollack and Hans Weiss reported that since the formation of the Communication Satellite Corp.,

  The cost of a telephone call has decreased by 12,000 percent.

  If a cost decreases by 100 percent, it drops to zero (no matter what the initial cost was). If a cost decreases by 200 percent, someone is paying you the same amount you used to pay them for you to take the product. A decrease of 100 percent is very rare; one of 12,000 percent seems wildly unlikely. An article in the peer-reviewed Journal of Management Development claimed a 200 percent reduction in customer complaints following a new customer care strategy. Author Dan Keppel even titled his book Get What You Pay For: Save 200% on Stocks, Mutual Funds, Every Financial Need. He has an MBA. He should know better.

  Of course, you have to apply percentages to the same baseline in order for them to be equivalent. A 50 percent reduction in salary cannot be restored by increasing your new, lower salary by 50 percent, because the baselines have shifted. If you were getting $1,000/week and took a 50 percent reduction in pay, to $500, a 50 percent increase in that pay only brings you to $750.

  Percentages seem so simple and incorruptible, but they are often confusing. If interest rates rise from 3 percent to 4 percent, that is an increase of 1 percentage point, or 33 percent (because the 1 percent rise is taken against the baseline of 3, so 1/3 = .33). If interest rates fall from 4 percent to 3 percent, that is a decrease of 1 percentage point, but not a decrease of 33 percent—it’s a decrease of 25 percent (because the 1 percentage point drop is now taken against the baseline of 4). Researchers and journalists are not always scrupulous about making this distinction between percentage point and percentages clear, but you should be.

  The New York Times reported on the closing of a Connecticut textile mill and its move to Virginia due to high employment costs. The Times reported that employment costs, “wages, worker’s compensation and unemployment insurance—are 20 times higher in Connecticut than in Virginia.” Is this plausible? If it were true, you’d think that there would be a mass migration of companies out of Connecticut and into Virginia—not just this one mill—and that you would have heard of it by now. In fact, this was not true and the Times had to issue a correction. How did this happen? The reporter simply misread a company report. One cost, unemployment insurance, was in fact twenty times higher in Connecticut than in Virginia, but when factored in with other costs, total employment costs were really only 1.3 times higher in Connecticut, not 20 times higher. The reporter did not have training in business administration and we shouldn’t expect her to. To catch these kinds of errors requires taking a step back and thinking for ourselves—which anyone can do (and she and her editors should have done).

  New Jersey adopted legislation that denied additi
onal benefits to mothers who have children while already on welfare. Some legislators believed that women were having babies in New Jersey simply to increase the amount of their monthly welfare checks. Within two months, legislators were declaring the “family cap” law a great success because births had already fallen by 16 percent. According to the New York Times:

  After only two months, the state released numbers suggesting that births to welfare mothers had already fallen by 16 percent, and officials began congratulating themselves on their overnight success.

  Note that they’re not counting pregnancies, but births. What’s wrong here? Because it takes nine months for a pregnancy to come to term, any effect in the first two months cannot be attributed to the law itself but is probably due to normal fluctuations in the birth rate (birth rates are known to be seasonal).

  Even so, there were other problems with this report that can’t be caught with plausibility checks:

  . . . over time, that 16 percent drop dwindled to about 10 percent as the state belatedly became aware of births that had not been reported earlier. It appeared that many mothers saw no reason to report the new births since their welfare benefits were not being increased.

  This is an example of a problem in the way statistics were collected—we’re not actually surveying all the people that we think we are. Some errors in reasoning are sometimes harder to see coming than others, but we get better with practice. To start, let’s look at a basic, often misused tool.

  The pie chart is an easy way to visualize percentages—how the different parts of a whole are allocated. You might want to know what percentage of a school district’s budget is spent on things like salaries, instructional materials, and maintenance. Or you might want to know what percentage of the money spent on instructional materials goes toward math, science, language arts, athletics, music, and so on. The cardinal rule of a pie chart is that the percentages have to add up to 100. Think about an actual pie—if there are nine people who each want an equal-sized piece, you can’t cut it into eight. After you’ve reached the end of the pie, that’s all there is. Still, this didn’t stop Fox News from publishing this pie chart: