WEAPONIZED LIES by Daniel J. Levitin

• There are not two sides to a story when one side is a lie.
• Truth matters. A post-truth era is an era of willful irrationality, reversing all the great advances humankind has made.
• Our information infrastructure is powerful. It can do good or it can do harm. And each of us needs to know how to separate the two.
• We are fortunate to have a free press; historically, most nations have had much worse. We should never take the media’s freedom and integrity for granted.
• We are a social species, and we tend to believe what others tell us.
• Some claims might be true, but truthful claims are true.
• Critical thinking trains us to take a step back, to evaluate facts and form evidence-based conclusions.
• The most important component of the best critical thinking that is lacking in our society today is humility. It is a simple yet profound notion: If we realize we don’t know everything, we can learn. If we think we know everything, learning is impossible.
• Misinformation is devilishly entwined on the Internet with real information, making the two difficult to separate.
• The unique problem we face today is that misinformation has proliferated and lies can be weaponized to produce social and political ends we would otherwise be safeguarded against.
• Critical thinking doesn’t mean we disparage everything; it means that we try to distinguish between claims with evidence and those without.
• Just because a statistic is cited doesn’t mean it’s relevant to the point at hand.
• Infoliteracy means being able to recognize 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 bad decisions and bad results.
• Statistics are not facts. They are interpretations.
• 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.
• Don’t just accept a claim at face value; work through it a bit.
• The cardinal rule of a pie chart is that the percentages have to add up to 100.
• An average can be a helpful summary statistic, even easier to digest than a pie chart, allowing us to characterize a very large amount of information with a single number.
• There are three ways of calculating an average, and they often yield different numbers, so people with statistical acumen usually avoid the word average in favor of the more precise terms mean, median, and mode.
• Remember, the point of an average is to be able to represent a whole lot of data with a single number.
• This is the problem with the mean: It is sensitive to outliers.
• In criminal trials, the way the information is presented—the framing—profoundly affects jurors’ conclusions about guilt.
• Be careful of averages and how they’re applied. One way that they can fool you is if the average combines samples from disparate populations.
• Also be careful to remember that the average doesn’t tell you anything about the range.
• The average can smear across differences that are important.
• The ecological fallacy occurs when we make inferences about an individual based on aggregate data (such as a group mean), and the exception fallacy occurs when we make inferences about a group based on knowledge of a few exceptional individuals.
• The human brain did not evolve to process large amounts of numerical data presented as text; instead, our eyes look for patterns in data that are visually displayed.
• Graphs come in two broad types: Either they represent every data point visually (as in a scatter plot) or they implement a form of data reduction in which we summarize the data, looking, for example, only at means or medians.
• There are many ways that graphs can be used to manipulate, distort, and misrepresent data.
• The most fundamental way to lie with a statistical graph is to not label the axes. If your axes aren’t labeled, you can draw or plot anything you want!
• A well-designed graph clearly shows you the relevant end points of a continuum. This is especially important if you’re documenting some actual or projected change in a quantity, and you want your readers to draw the right conclusions.
• When you have a situation of steady growth (or decline), the most accurate way to represent the data is on a logarithmic scale. The logarithmic scale allows equal percentage changes to be represented by equal distances on the y-axis.
• The graph maker can get away with all kinds of lies simply armed with the knowledge that most readers will not look at the graph very closely.
• Correlations range from −1 to 1. A correlation of 0 means that one variable is not related to the other at all. A correlation of -1 means that as one variable goes up, the other goes down, in precise synchrony. A correlation of 1 means that as one variable goes up, the other does too, also in precise synchrony.
• Just because someone quotes you a statistic or shows you a graph, it doesn’t mean it’s relevant to the point they’re trying to make.
• When two things are related, whether or not one causes the other, statisticians call it a correlation.
• Infographics are often used by lying weasels to shape public opinion, and they rely on the fact that most people won’t study what they’ve done too carefully.
• Statistical significance tests quantify how easily pure chance can explain the results.
• Interpolation takes two data points and estimates the value that would have occurred between them if you had taken a measurement there.
• When faced with the precision of numbers, we tend to believe that they are also accurate, but this is not the same thing.
• Access is one of those words that should raise red flags when you encounter them in statistics.
• One way to lie with statistics is to compare things—datasets, populations, types of products—that are different from one another, and pretend that they’re not.
• Be on the lookout for changing samples before drawing conclusions!
• Amalgamating is putting things that are different (heterogeneous) into the same bin or category—a
• Just because there’s a number on it, it doesn’t mean that the number was arrived at properly.
• To be any good, a sample has to be representative. A sample is representative if every person or thing in the group you’re studying has an equally likely chance of being chosen. If not, your sample is biased.
• Achieving an unbiased sample isn’t easy. When hearing a new statistic, ask, “What biases might have crept in during the sampling?”
• Margin of error and confidence interval apply to sampling of any kind,
• you can lie with statistics very easily by failing to report the margin of error or confidence interval.
• If you simply can’t reach some segment of the population, such as military personnel stationed overseas, or the homeless and institutionalized, this sampling bias is called coverage error because some members of the population from which you want to sample cannot be reached and therefore have no chance of being selected.
• People sometimes lie when asked their opinions.
• People don’t always tell the truth in surveys.
• Measurements must be standardized. There must be clear, replicable, and precise procedures for collecting data so that each person who collects it does it in the same way. Each person who is counting has to count in the same way.
• Measurement error occurs in every measurement, in every scientific field.
• Measurement error turns up whenever we quantify anything.
• How something is defined or categorized can make a big difference in the statistic you end up with.
• Whenever we encounter a news story based on new research, we need to be alert to how the elements of that research have been defined. We need to judge whether they are acceptable and reasonable.
• GIGO is a famous saying coined by early computer scientists: garbage in, garbage out.
• Much of what we read should raise our suspicions.
• Probabilities allow us to quantify future events and are an important aid to rational decision making.
• We use the word probability in different ways to mean different things.
• Subjective probability is the only kind of probability that we have at our disposal in practical situations in which there is no experiment, no symmetry equation.
• One of the most important rules in probability is the multiplication rule. If two events are independent—that is, if the outcome of one does not influence the outcome of the other—you obtain the probability of both of them happening by multiplying the two probabilities together.
• The multiplication rule only applies if the events are independent of one another.
• Often when looking at statistical claims, we’re led astray by examining an entire group of random people when we really should be looking at a subgroup.
• You can calculate the probabilities using the formula for Bayes’s rule (found in the Appendix), but an easy way to visualize and compute conditional probabilities is with the fourfold table, describing all possible scenarios:
• Most of us have difficulty figuring probabilities and statistics in our heads and detecting subtle patterns in complex tables of numbers. We prefer vivid pictures, images, and stories. When making decisions, we tend to overweight such images and stories, compared to statistical information. We also tend to misunderstand or misinterpret graphics.
• Lying weasels who want to separate us from our money, or get us to vote against our own best interests, will try to snow us with pseudo-facts, confuse us with numbers that have no basis, or distract us with information that, upon closer examination, is not actually relevant. They will masquerade as experts.
• A big part of the problem here is that the human brain often makes up its mind based on emotional considerations, and then seeks to justify them. And the brain is a very powerful self-justifying machine.
• Even the smartest of us can be fooled.
• Determining the truthfulness or accuracy of a source is not always possible.
• The first thing to do when evaluating a claim by some authority is to ask who or what established their authority.
• Experts talk in two different ways, and it is vital that you know how to tell these apart. In the first way, they review facts and evidence, synthesizing them and forming a conclusion based on the evidence. Along the way, they share with you what the evidence is, why it’s relevant, and how it helped them to form their conclusion.
• The second way experts talk is to just share their opinions. They are human. Like the rest of us, they can be given to stories, to spinning loose threads of their own introspections, what-ifs, and untested ideas.
• The term expert is normally reserved for people who have undertaken special training, devoted a large amount of time to developing their expertise (e.g., MDs, airline pilots, musicians, or athletes), and whose abilities or knowledge are considered high relative to others’. As such, expertise is a social judgment—we’re comparing one person’s skill to the skill level of other people in the world. Expertise is relative.
• Expertise also falls along a continuum.
• Experts are often licensed, or hold advanced degrees, or are recognized by other authorities.
• Some publications are more likely to consult true experts than others, and there exists a hierarchy of information sources. Some sources are simply more consistently reliable than others.
• Reputable sources want to be certain of facts before publishing them.
• As with graphs and statistics, we don’t want to blindly believe everything we encounter from a good source, nor do we want to automatically reject everything from a questionable source.
• People are not always who they appear to be on the Web.
• Knowing the domain name is helpful but hardly a foolproof verification system.
• Truth is the default position and we assume others are being truthful with us.
• When judging an expert, keep in mind that experts can be biased without even realizing it.
• A meta-analysis is a research technique whereby the results of dozens or hundreds of studies from different labs are analyzed together to determine the weight of evidence supporting a particular claim.
• A special Google search allows you to see who else links to a web page you land on. Type “link:” followed by the website URL, and Google will return all the sites that link to it.
• Peer review is not the only system to rely on, but it provides a good foundation in helping us to draw our own conclusions, and like democracy, it’s the best such system we have.
• On the Web, there is no central authority to prevent people from making claims that are untrue, no way to shut down an offending site other than going through the costly procedure of obtaining a court injunction.
• One way to fool people into thinking that you’re really knowledgeable is to find knowledgeable-sounding things on other people’s Web pages and post them to your own.
• Unscrupulous hucksters count on the fact that most people don’t bother reading footnotes or tracking down citations. This makes it really easy to lie.
• When evaluating evidence, people often ignore the numbers and axis labels, as we’ve seen, but they also often ignore the verbal descriptors, too.
• When evaluating a claim or argument, ask yourself if there is another reason—other than the one offered—that could account for the facts or observations that have been reported. There are always alternative explanations; our job is to weigh them against the one(s) offered and determine whether the person drawing the conclusion has drawn the most obvious or likely one.
• People who try to predict the future without using psychic powers—military leaders, economists, business strategists—are often wildly off in their predictions because they fail to consider alternative explanations.
• Alternative explanations are often critical to legal arguments in criminal trials.
• Our brains are built to make stories as they take in the vastness of the world with billions of events happening every second. There are apt to be some coincidences that don’t really mean anything.
• But if you’re looking only for supporting evidence, you’re not doing proper research, because you’re ignoring the contradictory evidence—there might be a little of this or a lot, but you don’t know because you haven’t looked. Colloquially, scientists call this “cherry-picking” the data that suit your hypothesis.
• Proper research demands that you keep an open mind about any issue, and try to valiantly consider the evidence for and against, and then form an evidence-based (not a “gee, I wish this were so”–based) conclusion.
• A companion to the cherry-picking bias is selective windowing. This occurs when the information you have access to is unrepresentative of the whole.
• When looking at data or evidence to support a claim, ask yourself if what you’re being shown is likely to be representative of the whole picture.
• Small samples are usually not representative.
• Larger samples more accurately reflect the state of the world. Statisticians call this the law of large numbers.
• When evaluating claims based on probabilities, try to understand the underlying model.
• Counterknowledge, a term coined by the U.K. journalist Damian Thompson, is misinformation packaged to look like fact and that some critical mass of people believes.
• Counterknowledge initially attracts us with the patina of knowledge and authority, but further examination shows that these have no basis in fact—the purveyors of counterknowledge are hoping you’ll be sufficiently impressed (or intimidated) by the presence of gritty assertions and numbers that you’ll blindly accept them.
• It’s important to accept that in complex events, not everything is explainable, because not everything was observed or reported.
• A handful of unexplained anomalies does not discredit or undermine a well-established theory that is based on thousands of pieces of evidence.
• The difference between a false theory and a true theory is one of probability.
• Absolute certainty in most news stories and scientific findings doesn’t exist. But as humans, we seek certainty. Demagogues, dictators, cults, and even some religions offer it—a false certainty—that many find irresistible.
• We assume that newspaper space given to crime reporting is a measure of crime rate.
• Cognitive psychologist Paul Slovic showed that people dramatically overweight the relative risks of things that receive media attention.
• Misunderstandings of risk can lead us to ignore or discount evidence we could use to protect ourselves.
• The main reason why so many people are dying of cancer is that they’re not dying of other things first. You have to die of something
• If you want to snow people with counterknowledge, one effective technique is to get a whole bunch of verifiable facts right and then add only one or two that are untrue.
• The fact is that bottled water is at best no safer or healthier than most tap water in developed countries, and in some cases less safe because of laxer regulations.
• The development of critical thinking over many centuries led to a paradigm shift in human thought and history: the scientific revolution.
• The search for proof, for certainty, drives science, but it also drives our sense of justice and all our judicial systems. Scientific practice has shown us the right way to proceed with this search.
• There are two pervasive myths about how science is done. The first is that science is neat and tidy, that scientists never disagree about anything. The second is that a single experiment tells us all we need to know about a phenomenon, that science moves forward in leaps and bounds after every experiment is published.
• Real science is replete with controversy, doubts, and debates about what we really know.
• Real scientific knowledge is gradually established through many replications and converging findings.
• Scientific progress depends on two kinds of reasoning.
• In deduction, we reason from the general to the specific, and if we follow the rules of logic, we can be certain of our conclusion.
• In induction, we take a set of observations or facts, and try to come up with a general principle that can account for them. This is reasoning from the specific to the general.
• In abductive reasoning, we start with a set of observations and then generate a theory that accounts for them. Of the infinity of different theories that could account for something, we seek the most likely.
• The brain is a giant pattern detector, and it seeks to extract order and structure from what often appear to be random configurations.
• An odd feature of human cognition is that once we form a belief or accept a claim, it’s very hard for us to let go, even in the face of overwhelming evidence and scientific proof to the contrary.
• A properly formulated scientific hypothesis is falsifiable—there are steps we can take, at least in theory, to test the true state of the world, to determine if our hypothesis is true or not. In practice, this means considering alternative explanations ahead of time, before conducting the experiment, and designing the experiment so that the alternatives are ruled out.
• The unknown unknowns are the most dangerous.
• One of the main purposes of training someone for a PhD, a law or medical degree, an MBA, or military leadership is to teach them to identify and think systematically about what they don’t know, to turn unknown unknowns into known unknowns.
• One of the biggest causes of bad, even fatal, outcomes is belief in things that are untrue.
• Using Bayes’s rule allows us to combine objective probabilities,
• Critical thinking is something that can be taught, and practiced, and honed as a skill.
• There is an infinite variety of ways that faulty reasoning and misinformation can sneak up on us. Our brains weren’t built to excel at this.
• It sounds counterintuitive, but it’s true: Not all treatments actually help.
• If you want to convince people of something that’s not true, it’s apparently very effective to simply snow them with one question after another, and hope that they will be sufficiently impressed—and overwhelmed—that they won’t bother to look for explanations.
• Professional magicians typically calculate and plan everything they say. Every single move, every apparently spontaneous scratch of the head, is typically rehearsed over and over again.
• A lot of what magicians practice over and over again is getting the audience to accept things that are a bit out of the ordinary.
• Expertise tends to be narrow.
• The scientific method is the ground from which all the best critical thinking rises.
• Critical thinking is not something you do once with an issue and then drop it. It’s an active and ongoing process. It requires that we all think like Bayesians, updating our knowledge as new information comes in.
• We’re far better off knowing a moderate number of things with certainty than a large number of things that might not be so.