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▪ pag. 5 - In his book Chartism, published in 1840, the social critic Thomas Carlyle noted: “A witty statesman said you might prove anything with figures.”

▪ pag. 8 - “inferential statistics,” complex forms of reasoning that we will ignore.

▪ pag. 9 - in 1833, for instance, reformers published a report declaring that there were “not less than 10,000” prostitutes in NewYork (equivalent to about 10 percent of the city’s female population); in 1866, New York’s Methodist bishop claimed there were more prostitutes (11,000 to 12,000) than Methodists in the city; other estimates for the period ranged as high as 50,000. These reformers hoped that their reports of widespread prostitution would prod the authorities to act, but city officials’ most common response was to challenge the reformers’ numbers.

▪ pag. 10 - During Ronald Reagan’s presidency, for example, activists claimed that three million Americans were homeless, while the Reagan administration insisted that the actual number of homeless people was closer to 300,000, one-tenth what the activists claimed.

▪ pag. 11 - in seventeenth-century Europe, particularly in England and France. Analysts tried to count births, deaths, and marriages because they believed that a growing population was evidence of a healthy state; those who conducted such numeric studies–as well as other, nonquantitative analyses of social and political prosperity–came to be called statists. Over time, the statists’ social research led to the new term for quantitative evidence: statistics.

▪ pag. 13 - Numbers are created and repeated because they supply ammunition for political struggles, and this political purpose is often hidden behind assertions that numbers, simply because they are numbers, must be correct. People use statistics to support particular points of view, and it is naive simply to accept numbers as accurate, without examining who is using them and why.

▪ pag. 18 - In virtually every case, promoters use statistics as ammunition; they choose numbers that will draw attention to or away from a problem, arouse or defuse public concern.

▪ pag. 18 - bad statistics live on; they take on lives of their own.

▪ pag. 19 - Some statistics are born bad–they aren’t much good from the start, because they are based on nothing more than guesses or dubious data. Other statistics mutate; they become bad after being mangled.

▪ pag. 22 - All statistics, even the most authoritative, are created by people.This does not mean that they are inevitably flawed or wrong, but it does mean that we ought to ask ourselves just how the statistics we encounter were created.

▪ pag. 26 - even official statistics are social products, shaped by the people and organizations that create them.

questo è un punto di partenza

▪ pag. 26 - If the domestic dispute call comes near the end of the officers’ shift, they may favor quick solutions. If their department has a new policy to crack down on domestic disputes, officers will be more likely to make arrests. All these decisions, each shaped by various considerations, will affect whatever statistics eventually summarize the officers’ actions.

▪ pag. 27 - 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.

▪ pag. 27 - There are three basic questions that deserve to be asked whenever we encounter a new statistic. 1. Who created this statistic? […] 2. Why was this statistic created? […] 3. How was this statistic created?

▪ pag. 28 - There is a big difference between a number produced by a wild guess, and one generated through carefully designed research. \

▪ pag. 45 - Public attitudes toward most social issues are too complex to be classified in simple pros and cons

▪ pag. 48 - For example, the order in which questions are asked can make a difference in how people respond.

▪ pag. 50 - Remember that activists usually believe that the problem they seek to bring to public attention is both large and largely unrecognized, that there is a substantial dark figure of hidden cases. […] Therefore, they devise measurements that will minimize false negatives.

▪ pag. 50 - Often, measurement decisions are hidden.

▪ pag. 53 - the representativeness of a sample is actually far more important than sample size.

▪ pag. 59 - First, good statistics are based on more than guessing. […] Watch for the danger signs of guessing: Do the people offering the statistic have a bias–do they want to show that the problem is common (or rare)? Is the statistic a big, round number? Does the statistic describe an unfamiliar, hidden social problem that probably has a large dark figure (if so, how did the advocates manage to come up with their numbers)? […] Second, good statistics are based on clear, reasonable definitions. […] Third, good statistics are based on clear, reasonable measures. […] Finally, good statistics are based on good samples. […] One sign of good statistics is that we’re given more than a number; we’re told something about the definitions, measurement, and sampling behind the figure-

▪ pag. 62 - Numbers–even good numbers–can be misunderstood or misinterpreted.Their meanings can be stretched, twisted, distorted, or mangled. These alterations create what we can call mutant statistics–distorted versions of the original figures. Many mutant statistics have their roots in innumeracy. […] The general public may be innumerate, but often the advocates promoting social problems are not any better.

▪ pag. 63 - Once someone utters a mutant statistic, there is a good chance that those who hear it will accept it and repeat it. Innumerate advocates influence their audiences: the media repeat mutant statistics; and the public accepts–or at least does not challenge–whatever numbers the media present.

▪ pag. 63 - Anorexia typically affects young women. In the United States each year, roughly 8,500 females aged 15­24 die from all causes; another 47,000 women aged 25­44 also die. What were the chances, then, that there could be 150,000 deaths from anorexia each year?

▪ pag. 64 - Once created, mutant statistics have a good chance of spreading and enduring.

▪ pag. 78 - some statistics get mangled because they seem too difficult to grasp, and therefore they are easily confused.

▪ pag. 80 - Only 15 percent of the new entrants to the labor force over the next 13 years will be native white males, compared to 47 percent in that category today.”18 That sentence was wrong for two reasons: first, it confused net additions to the labor force (expected to be roughly 15 percent white males) with all new entrants to the labor force (white males were expected to be about 32 percent of all those entering the labor force); and, second, it made a meaningless comparison between the percentage of white males among net workforce entrants and white males’ percentage in the existing labor force (roughly 47 percent).

▪ pag. 82 - complex statistics are prime candidates for mutation.

▪ pag. 92 - Most social scientists consider Kinsey’s 10 percent estimate for homosexuality too high; more recent, more reliable studies have consistently produced lower estimates. Yet some gay and lesbian activists continue to cite the higher figure–precisely because it is the largest available number. In turn, 10 percent often figures into other calculations–not just about gay teen suicides, but also regarding the number of gay voters, the size of the gay population at risk of AIDS, and so on.

▪ pag. 95 - Once in circulation, a mutant statistic is difficult to retract.

▪ pag. 116 - The first advocate] says “Whites account for more than 80 percent of the victims of violent crime, yet blacks are arrested at a rate roughly five times greater than whites”; while the second advocate says, “Blacks are victimized at a rate more than 25 percent higher than whites, yet most people arrested for crimes of violence are white.”

▪ pag. 118 - Most social scientists who compare whites and blacks do not assume that race in and of itself–that is, the biological differences among races– is especially important. Rather, they treat race as a crude measure of social class.

questo vale anche in Italia per i crimini con immigrati

▪ pag. 129 - Any number–even the most implausible figure (for example, 50,000 stranger abductions)– can survive if it goes unchallenged.

bisogna ricordarlo sempre

▪ pag. 129 - Stat wars indicate that someone cares enough to dispute a statistic. Usually these debates reflect the opponents’ competing interests.

il guaio è che il pubblico sceglie la posizione di chi parla più forte

▪ pag. 149 - How is it possible for the average income per person to rise at the same time the average hourly wage fell? Changes in the workforce help account for this apparent discrepancy. Most important, the proportion of the population in the workforce grew, in particular, the proportion of employed women rose.

▪ pag. 150 - It is important to appreciate that these measures of growing inequality usually do not track particular individuals through time. That is, when we compare, say, the poorest fifth of the population in Year 1 with the poorest fifth ten years later, we are not necessarily talking about the same people; some poor people experience upward mobility.

▪ pag. 153 - Still, our society makes it easy to create and spread statistics about social problems. This is important because we often equate numbers with“facts.”Treating a number as a fact implies that it is indisputable. It should be no suprise, then, when people interested in some social problem collect relevant statistics and present them as facts.This is a way for them to claim authority, to argue that the facts (“It’s true!”) support their position.

▪ pag. 159 - The best response to stat wars is not to try and guess who’s lying or, worse, simply to assume that the people we disagree with are the ones telling lies. Rather, we need to watch for the standard causes of bad statistics–guessing, questionable definitions or methods, mutant numbers, and inappropriate comparisons. In some cases, we may conclude that one number is right and another is deeply flawed; in others, we may discover that the different figures reflect people choosing different methods to answer different questions. Whatever we conclude, we should come away with a better understanding of all the statistics.

▪ pag. 160 - There are cultures in which people believe that some objects have magical powers; anthropologists call these objects fetishes. In our society, statistics are a sort of fetish.

▪ pag. 161 - Still, I hope that, having read this book, you have become more familiar with some of the most common flaws that bedevil social statistics: that you can ask some basic questions about a statistic’s origins (definition, measurement, sampling, and the other issues covered in chapter 2); that you are familiar with some of the ways statistics can be mangled (chapter 3); that you understand the risks of inappropriate comparisons (chapter 4); and that you can do more than simply throw up your hands when confronted with a debate featuring competing statistics

Ottimo riassunto

▪ so many people in our society treat statistics as fetishes. We might call this the mind-set of the Awestruck–the people who don’t think critically, who act as though statistics have magical powers.The Awestruck know they don’t always understand the statistics they hear, but this doesn’t bother them. After all, who can expect to understand magical numbers?

▪ pag. 162 - The Naive are slightly more sophisticated than the Awestruck. Many people believe they understand a bit about statistics–they know something about percentages, rates, and the like–but their approach is basically accepting.They presume that statistics are generally accurate, that they mean what they seem to mean. The Naive are often at least somewhat innumerate;

▪ pag. 163 - In addition to creating, spreading, and mangling statistics, the Naive (and their slightly less critical cousins, the Awestruck) probably account for the vast majority of the audience that hears these numbers. The Naive are unlikely to question numbers–not even the most implausible exaggerations; after all, the Naive usually don’t suspect statistics might be bad, and even if they do, they have no good ways of detecting bad statistics.

▪ pag. 164 - The Cynical are suspicious of statistics; they are convinced that numbers are probably flawed, and that those flaws are probably intentional.They view statistics as efforts to manipulate–they are worse than “damned lies.”

▪ pag. 164 - The Cynical design research that will produce the results they want:

▪ pag. 166 - The Critical approach statistics thoughtfully; they avoid the extremes of both naive acceptance and cynical rejection of the numbers they encounter. Instead, the Critical attempt to evaluate numbers, to distinguish between good statistics and bad statistics.

mi pare troppo cooked up

▪ pag. 175 - Numbers imply that a claim is factual. Citing an obviously incorrect number makes one seem ignorant, foolish, silly, even dishonest. And backpedaling to say that the obviously incorrect number “was not intended to be a factual statement,” only makes things worse, by suggesting that one doesn’t know–or doesn’t see the need to play by–the rules for acceptable rhetoric.

mi pare ottimista: per chi un numero è “ovviamente” farlocco? e siamo così certi che anche mostrando che un numero è farlocco la gente non pensi semplicemente che siamo solo dei rompipalle?

▪ pag. 179 - Not surprisingly, a large share of numeracy advocates are educators, often teachers or professors of mathematics, or education school faculty who specialize in mathematics education; others come from other disciplines, such as economics or geology, but tend to teach courses in statistics or quantitative methods.

bayesianamente, se non hai idea di numeratezza non puoi esserne un proponente

▪ pag. 181 - Bad statistics are not simply the result of deficient math skills; very often, they are not caused by someone making incorrect calculations. Senator Kyl did not make a mistake while calculating abortion’s share of Planned Parenthood’s services, any more than the blogger miscalculated the number of smoking-related deaths in California. Both presented numbers–inviting them to be understood as facts–without giving much thought to what they were saying.

fosse solo con i numeri…