On sex, lies and the pitfalls of overblown statistics (John Kay)

2011年09月01日 06:21 AM
統計數據的誤區
On sex, lies and the pitfalls of overblown statistics
作者：英國《金融時報》專欄作家約翰•凱





A visit to the International Festival of Statistics in Dublin (yes, really) prompted me to offer advice to young scholars on the interpretation and use of economic data.


參加完於都柏林舉行的國際統計節(International Festival of Statistics)——是的，真有這樣的活動——我忍不住想就對經濟數據的解讀和使用向年輕學者們提點建議。

Always ask yourself the question “where does that data come from?”. “Long distance rail travel in Britain is expected to increase by 96 per cent by 2043.” Note how the passive voice “is expected” avoids personal responsibility for this statement. Who expects this? And what is the basis of their expectation? For all I know, we might be using flying platforms in 2043, or be stranded at home by oil shortages: where did the authors acquire their insight?


永遠不忘問你自己這樣一個問題：“數據從何而來？”。 “到2043年，英國的長途鐵路旅行據預計將增長96%。”注意這裡的被動語態“據預計”是如何撇清了個人對於這一陳述的責任的。誰預計了這個增長？他們做出這一預計的根據又是什麼？就我所知，到2043年，我們可能已經在使用飛行平台了，或者因為石油短缺而被困在家裡。這句話的作者又是從哪裡獲得這種見解的呢？

“On average, men think about sex every seven seconds.” How did the researchers find this out? Did they ask men how often they thought about sex, or when they last thought about sex (3½ seconds ago, on average)? Did they give their subjects a buzzer to press every time they thought about sex? How did they confirm the validity of the responses? Is it possible that someone just made this statement up, and it has been repeated without attribution ever since? Many of the numbers I hear at business conferences have that provenance.


“男性平均每7秒就會想到性。”研究人員是如何得出這一結論的？是通過詢問男性多久會想到性，還是問他們上次想到性是什麼時候（平均3.5秒前）？他們有沒有給被採訪對像一個蜂鳴器，讓他們每次一想到性就按一下？他們如何確認那些反應的真實性？有沒有可能這不過是某個人編造出來的說法，然後就在未經求證來源的情況下被重複使用了？我在商務會議上聽到的許多數字都是這麼來的。

In more intellectual environments, the figures presented may be the product of serious analysis and calculation. Always ask of such data “what is the question to which this number is the answer?”. “Earnings before interest, tax, depreciation and amortisation on a like-for-like basis before allowance for exceptional restructuring costs” is the answer to the question “what is the highest profit number we can present without attracting flat disbelief?”.


在學術性更強的環境中，人們拿出的數字也許是嚴肅分析與計算的產物。關於這些數據，永遠要問這樣一個問題，“這些數字想要回答什麼問題？”。 “不 考慮預提特殊重組成本、在可比基礎上的稅息折舊及攤銷前利潤(Earnings before interest, tax, depreciation and amortisation on a like-for-like basis before allowance for exceptional restructuring costs)”是要回答“在不招致嚴重懷疑的情況下，我們所能報告的最高盈利數字”這個問題嗎？

Beware explanations that are tautological: “gross domestic product is a measure of the income of the nation”, “movements of the consumer prices index reflect changes in the cost of a basket of commodities compiled by the Office for National Statistics”. Always probe descriptions – “GDP is not a measure of output, or of welfare” – that define what a statistic is not, rather than what it is. “These figures are not forecasts, and should not be relied on by prospective investors.” If they are not forecasts, then what are they, and if they are not to be relied on by prospective investors what purpose was intended in distributing the information to them?


要當心一些同義反复的解釋：“國內生產總值是衡量一國收入的指標”，“消費者價格指數變動反映的是國家統計局編制的一籃子商品的成本變化”。永遠記得深入探究界定某個統計數據不是什麼、而非是什麼的描述，比如“GDP不是衡量產出或福利的指標”；“這些數字不是預測數字，潛在投資者不應完全依賴這些數字。”如果它們不是預測數字，那是什麼；如果潛在投資者不能完全依賴這些數字，那向投資者傳遞這些信息的目的又何在？

Be careful of data defined by reference to other documents which you are expected not to have read. “These accounts have been compiled in accordance with generally accepted accounting principles”, or “these estimates are prepared in line with guidance given by HM Treasury and the Department of Transport”. Such statements are intended to give a false impression of authoritative endorsement. A data set compiled


小心那些定義中參照了其它料想你沒有讀過的文件的數據。 “這些賬目是​​根據普遍接受的會計原則編制的”，或者“上述估值是根據英國財政部(HM Treasury)與交通部(Department of Transport)發布的指導文件做出的”。這些陳述旨在給人們一個錯誤的印象——即它們已得到權威部門的認可。由國家性統計機構或受尊重的國際性機構（比如經濟合作與發展組織(OECD)或歐盟統計局(Eurostat)）編制的成套數據，應該是認真編制的​​。但這並不意味著，這些數據所包含的意思與使用它們的人所認為或所聲稱的那個意思相同。

by a national statistics organisation or a respected international institution such as the Organisation for Economic Co-operation and Development or Eurostat will have been compiled conscientiously.


當數據似乎指向一個意外的發現時，永遠記得考慮這樣一個可能性：即問題出在數據身上、而非世界本身。我最近看到了一項有關金融服務業相對生產力的研究，其中，意大利排名第一，英國與美國墊底。你或許會認為這會給我們敲響警鐘，但事實並非如此：作者接下來解釋說，差距如此之大，原因在於英美兩國金融服務業的規模。只要稍微思考一下，研究人員就應該注意到這樣一些問題，比如：”金融服務業產出的含義是什麼？“。

That does not, however, imply


但現在人們很容易不加思考就往計算機程序裡輸入數據。前文中對英國鐵路交通增長沒有根據的精確預測（96%而不是增加1倍）就暗示了，這個數字是由計算機、而非經驗豐富的證據解讀員得出。

that the numbers mean what the person using them thinks or asserts they mean.


統計數據的可靠程度，有賴於它們的出處以及使用者的能力。如果我在數據中發現了令人吃驚的東西，那最有可能的解釋是：我弄錯了。

When the data seem to point to an unexpected finding, always consider the possibility that the problem is a feature of the data, rather than a feature of the world. I recently saw a study of comparative productivity in financial services in which Italy came top and Britain and the US bottom. You might have thought alarm bells would ring, but no: the authors went on to comment that this divergence was serious because of the size of the financial services sectors of Britain and the US. A little thought might have directed the researchers' attention to questions such as “what is meant by output of financial services?”.