Damned Lies and Statistics. Joel Best

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СКАЧАТЬ with domestic disputes reveals that officials make decisions (relatively straightforward for marriage records, more complicated for coroners, and far less clear-cut in the case of the police), that official statistics are by-products of those decisions (police officers probably give even less thought than coroners to the statistical outcomes of their decisions), and that organizational practices form the context for those decisions (while there may be relatively little variation in how marriage records are kept, organizational practices likely differ more among coroners’ offices, and there is great variation in how police deal with their complex decisions, with differences among departments, precincts, officers, and so on). In short, even official statistics are social products, shaped by the people and organizations that create them.

      THINKING ABOUT STATISTICS AS SOCIAL PRODUCTS

      The lesson should be clear: statistics—even official statistics such as crime rates, unemployment rates, and census counts—are products of social activity. We sometimes talk about statistics as though they are facts that simply exist, like rocks, completely independent of people, and that people gather statistics much as rock collectors pick up stones. This is wrong. All statistics are created through people’s actions: people have to decide what to count and how to count it, people have to do the counting and the other calculations, and people have to interpret the resulting statistics, to decide what the numbers mean. All statistics are social products, the results of people’s efforts.

      Once we understand this, it becomes clear that we should not simply accept statistics by uncritically treating numbers as true or factual. If people create statistics, then those numbers need to be assessed, evaluated. Some statistics are pretty good; they reflect people’s best efforts to measure social problems carefully, accurately, and objectively. But other numbers are bad statistics—figures that may be wrong, even wildly wrong. We need to be able to sort out the good statistics from the bad. There are three basic questions that deserve to be asked whenever we encounter a new statistic.

      1. Who created this statistic? Every statistic has its authors, its creators. Sometimes a number comes from a particular individual. On other occasions, large organizations (such as the Bureau of the Census) claim authorship (although each statistic undoubtedly reflects the work of particular people within the organization).

      In asking who the creators are, we ought to be less concerned with the names of the particular individuals who produced a number than with their part in the public drama about statistics. Does a particular statistic come from activists, who are striving to draw attention to and arouse concern about a social problem? Is the number being reported by the media in an effort to prove that this problem is newsworthy? Or does the figure come from officials, bureaucrats who routinely keep track of some social phenomenon, and who may not have much stake in what the numbers show?

      2. Why was this statistic created? The identities of the people who create statistics are often clues to their motives. In general, activists seek to promote their causes, to draw attention to social problems. Therefore, we can suspect that they will favor large numbers, be more likely to produce them and less likely to view them critically. When reformers cry out that there are many prostitutes or homeless individuals, we need to recognize that their cause might seem less compelling if their numbers were smaller. On the other hand, note that other people may favor lower numbers. Remember that New York police officials produced figures showing that there were very few prostitutes in the city as evidence they were doing a good job. We need to be aware that the people who produce statistics often care what the numbers show, they use numbers as tools of persuasion.

      3. How was this statistic created? We should not discount a statistic simply because its creators have a point of view, because they view a social problem as more or less serious. Rather, we need to ask how they arrived at the statistic. All statistics are imperfect, but some are far less perfect than others. There is a big difference between a number produced by a wild guess, and one generated through carefully designed research. This is the key question. Once we understand that all social statistics are created by someone, and that everyone who creates social statistics wants to prove something (even if that is only that they are careful, reliable, and unbiased), it becomes clear that the methods of creating statistics are key. The remainder of this book focuses on this third question.

      PLAN OF THE BOOK

      The following chapters discuss some of the most common and important problems with the creation and interpretation of social statistics. Chapter 2 examines four basic sources of bad statistics: bad guesses, deceptive definitions, confusing questions, and biased samples. Chapter 3 looks at mutant statistics, at ways even good statistics can be mangled, misused, and misunderstood. Chapter 4 discusses the logic of statistical comparison and explores some of the most common errors in comparing two or more time periods, places, groups, or social problems. Chapter 5 considers debates over statistics. Finally, chapter 6 examines three general approaches to thinking about statistics.

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*I am not implying that there is anything wrong with calling attention to social problems. In fact, this book can be seen as my effort to construct “bad statistics” as a problem that ought to concern people.

      2

      SOFT FACTS

       Sources of Bad Statistics

      A child advocate tells Congress that 3,000 children per year are lured with Internet messages and then kidnapped. Tobacco opponents attribute over 400,000 deaths per year to smoking. Antihunger activists say that 31 million Americans regularly “face hunger.” Although the press tends to present such statistics as facts, someone, somehow, had to produce these numbers. But how? Is there some law enforcement agency that keeps track of which kidnappings begin with online seductions? Are there medical authorities who decide which lung cancer deaths are caused by smoking, and which have other causes, such as breathing polluted air? Who counts Americans facing hunger—and what does “facing hunger” mean, anyway?

      Chapter 1 argued that people produce statistics. Of course they do. All human knowledge—including statistics—is created through people’s actions; everything we know is shaped by our language, culture, and society. Sociologists call this the social construction of knowledge. Saying that knowledge is socially constructed does not mean that all we know is somehow fanciful, arbitrary, flawed, or wrong. For example, scientific knowledge can be remarkably accurate, so accurate that we may forget the people and social processes that produced it. I’m writing this chapter on a computer that represents the accumulation of centuries of scientific knowledge. Designing and building this computer required that people come to understand principles of physics, chemistry, electrical engineering, computer science—who knows what else? The development of that knowledge was a social process, yet the fact that the computer works reliably reflects the great confidence we have in the knowledge that went into building it.

      This is one way to think about facts. Knowledge is factual when evidence supports it and we have great confidence in its accuracy. What we call “hard fact” is information supported by strong, convincing evidence; this means evidence that, so far as we know, we cannot deny, however we examine or test it. Facts always can be questioned, but they hold up under questioning. How did people come by this information? How did they interpret it? Are other interpretations possible? The more satisfactory the answers to such questions, the “harder” the facts.

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