托福双语阅读素材：谁的话也别盲信

兔斯基

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谁的话也别盲信
一堂教你理解数字的速成课


Statistics
Nullius in verba
A crash course in understanding numbers


PEOPLE take in five times as much information each day as they did in the mid-1980s. With all these data sloshing泼around it is easy to feel lost. One politician uses a statistic to back up her argument; a newspaper uses another fact to refute it; an economist uses a third to prove them both wrong. In “A Field Guide to Lies and Statistics” Daniel Levitin, an American neuroscientist, shows the reader how to find a way through all this numerical confusion. 如今，人们每天吸收的信息量是上世纪80年代中期的五倍。置身于如此纷繁奔涌的数据之中(写作句型)，很容易就会茫然无措。一名政客用一个统计数字来支撑自己的论调，一家报纸随后用另一个事实驳斥了她，一位经济学家再用第三个证据证明这二人都错了。在《关于谎言与统计数字的实地指南》(A Field Guide to Lies and Statistics)一书中，美国神经科学家丹尼尔·列维京(Daniel Levitin)向读者展示了如何在这些数字迷阵中探寻出一条道路。


A book about statistics can easily be boring. Fortunately, Mr Levitin is the perfect guide. Before becoming an academic he used to work as a stand-up comedian. Drawing on those skills Mr Levitin peppers(小词活用)his book with wisecracks. He uses the phrase “on average, humans have one testicle” to make the point that the mean can be a misleading description of a population. He goes off on interesting tangents突然转移话题/突兀的转向, granting the reader some light relief from detailed analysis of sampling and probabilities. Only occasionally is his hokey style annoying. 统计学方面的书很容易就让人觉得无趣，幸好有列维京这样的完美向导。在投身学术之前，他从事过脱口秀喜剧表演。有了这方面的技巧，他在书中处处都写下了妙趣横生的语句。比如，他用这样一句话来点明用平均值来描述一个群体会多么有误导性：“人类平均每人有一颗睾丸。”他时不时会突然离题讲些趣事，令读者跳脱出关于抽样和概率的详细分析而得到些许轻松。偶尔，他这种略嫌造作的风格也会让人腻烦。


Using plenty of examples, Mr Levitin shows how easily statistics can lead people astray. Take the following assertion, which on a quick skim might seem perfectly reasonable: “In the 35 years since marijuana laws stopped being enforced in California, the number of marijuana smokers has doubled every year.” One will soon realise that this must be nonsense; even with only one smoker to begin with, after doubling every year for 35 years there would be more than 17bn of them. Mr Levitin repeatedly throws these statistical curveballs at his readers, training them to adopt a take-nobody’s-word-for-it attitude. It is an effective pedagogical technique.列维京运用大量实例来证明统计数据轻易就能让人们偏离真相。例如，粗看之下，下面这个言之凿凿的说法似乎十分合理：“自从加州停止实施大麻管理法以来，35年间吸食大麻的人每年都翻一倍。”听者很快就会意识到，这一定是在胡说。就算吸大麻人的一开始只有一个，每年翻一番，35年后也会有超过170亿人吸食大麻。列维京屡屡出其不意地向读者抛出这类统计学难题(别老prob..diffi..)，训练他们养成这样一种态度：任何人的话都不照单全收。这是一种行之有效的教学技巧。


Some statistics turn out to be plain wrong, but more commonly they mislead. Yet this is hard to spot: numbers appear objective and apolitical. A favourite of academics and journalists, when analysing trends, is to “rebase” their figures to 100 so as to back up the argument that they wish to make. For instance, starting a chart of American GDP growth in 2009, when the country was in recession, tricks the reader into thinking that over the long term the economy is stronger than it really is. “[K]eep in mind that experts can be biased without even realising it,” Mr Levitin reminds people. 有些统计数字到头来完全就是错的，不过它们误导人的情形要更为常见。然而，要辨认看似客观且无关政治的数字并不容易。在分析趋势的时候，学者和新闻记者们最喜欢做的一件事就是将所获数字的基数“重定”在100，好支撑自己想要说明的论点。举个例子，将美国GDP增长图表的起始时间定在该国陷入衰退的2009年，读者就会被蒙蔽，得出长期而言经济强健的印象，而这一印象会好于真实情况。列维京提醒人们，“记住，专家也会有偏见，而且还毫不自知。”


A basic understanding of statistical theory helps the reader cope with the onslaught of information. Mr Levitin patiently explains the difference between a percentage change and a percentage-point change, a common source of confusion. When a journalist describes a statistical result as “significant”, this rarely carries the same meaning as when a statistician says it. The journalist may mean that the fact is interesting. The statistician usually means that there is a 95% probability that the result has not occurred by chance. (Whether it is interesting or not is another matter.) 对统计学理论有了基本的了解，读者就能更好地应对海量信息的冲击。人们常常都会混淆百分比变化与百分点变化，列维京耐心地解释了二者的区别。当一名新闻记者用“显著的”(significant)来描述一个统计结果时，表达的意思很少会和统计学家在使用这个词时所指的意思相同。记者想表达的也许是这个事实很有趣，而统计学家指的通常都是该结果有95%的概率不是随机发生(写作句型)。(至于有趣与否就是另外一码事了。)


Some readers may find Mr Levitin’s book worthy but naive. The problem with certain populist politicians is not that they mislabel an x-axis here or fail to specify a control group there. Rather they deliberately promulgate blatant lies which play to voters’ irrationalities and insecurities. Yet if everyone could adopt the level of healthy statistical scepticism that Mr Levitin would like, political debate would be in much better shape. This book is an indispensable trainer.有些读者也许会认为列维京的这本书虽值得一读，但未免天真。某些民粹主义政客的问题并不在于他们在这里误标了x轴、在那里没有明确列出控制组，而是故意散布赤裸裸的谎言，迎合选民的非理性和不安情绪。不过，如果每个人都能对统计数字采取列维京所推崇的那种明智的怀疑态度，政治辩论的状况定会大有改观。这本书是一位不可或缺的教练员。


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