誰的話也別盲信
壹堂教妳理解數字的速成課
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潑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，人們每天吸收的信息量是上世紀80年代中期的五倍。置身於如此紛繁奔湧的數據之中(寫作句型)，很容易就會茫然無措。壹名政客用壹個統計數字來支撐自己的論調，壹家報紙隨後用另壹個事實駁斥了她，壹位經濟學家再用第三個證據證明這二人都錯了。在《關於謊言與統計數字的實地指南》(A Field Guide to Lies and Statistics)壹書中，美國神經科學家丹尼爾·列維京(Daniel Levitin)向讀者展示了如何在這些數字迷陣中探尋出壹條道路。
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(小詞活用)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突然轉移話題/突兀的轉向, granting the reader some light relief from detailed analysis of sampling and probabilities. Only occasionally is his hokey style annoying. 統計學方面的書很容易就讓人覺得無趣，幸好有列維京這樣的完美向導。在投身學術之前，他從事過脫口秀喜劇表演。有了這方面的技巧，他在書中處處都寫下了妙趣橫生的語句。比如，他用這樣壹句話來點明用平均值來描述壹個群體會多麽有誤導性：“人類平均每人有壹顆睪丸。”他時不時會突然離題講些趣事，令讀者跳脫出關於抽樣和概率的詳細分析而得到些許輕松。偶爾，他這種略嫌造作的風格也會讓人膩煩。
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.列維京運用大量實例來證明統計數據輕易就能讓人們偏離真相。例如，粗看之下，下面這個言之鑿鑿的說法似乎十分合理：“自從加州停止實施大麻管理法以來，35年間吸食大麻的人每年都翻壹倍。”聽者很快就會意識到，這壹定是在胡說。就算吸大麻人的壹開始只有壹個，每年翻壹番，35年後也會有超過170億人吸食大麻。列維京屢屢出其不意地向讀者拋出這類統計學難題(別老prob..diffi..)，訓練他們養成這樣壹種態度：任何人的話都不照單全收。這是壹種行之有效的教學技巧。
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. 有些統計數字到頭來完全就是錯的，不過它們誤導人的情形要更為常見。然而，要辨認看似客觀且無關政治的數字並不容易。在分析趨勢的時候，學者和新聞記者們最喜歡做的壹件事就是將所獲數字的基數“重定”在100，好支撐自己想要說明的論點。舉個例子，將美國GDP增長圖表的起始時間定在該國陷入衰退的2009年，讀者就會被蒙蔽，得出長期而言經濟強健的印象，而這壹印象會好於真實情況。列維京提醒人們，“記住，專家也會有偏見，而且還毫不自知。”
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.) 對統計學理論有了基本的了解，讀者就能更好地應對海量信息的沖擊。人們常常都會混淆百分比變化與百分點變化，列維京耐心地解釋了二者的區別。當壹名新聞記者用“顯著的”(significant)來描述壹個統計結果時，表達的意思很少會和統計學家在使用這個詞時所指的意思相同。記者想表達的也許是這個事實很有趣，而統計學家指的通常都是該結果有95%的概率不是隨機發生(寫作句型)。(至於有趣與否就是另外壹碼事了。)
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.有些讀者也許會認為列維京的這本書雖值得壹讀，但未免天真。某些民粹主義政客的問題並不在於他們在這裏誤標了x軸、在那裏沒有明確列出控制組，而是故意散布赤裸裸的謊言，迎合選民的非理性和不安情緒。不過，如果每個人都能對統計數字采取列維京所推崇的那種明智的懷疑態度，政治辯論的狀況定會大有改觀。這本書是壹位不可或缺的教練員。