"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954....
"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton
Darrell Huff (July 15, 1913 – June 27, 2001) was an American writer, and is best known as the author of How to Lie with Statistics (1954), the best-selling statistics book of the second half of the twentieth century, and for his use of statistics as a tobacco lobbyist.
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1 有用 林@语堂 2017-05-02 19:00:00
一本小书，科普统计思维，了解统计方法用的不对时会带来的误导性结论。常识性防骗手册。不过有点啰嗦。
5 有用 20几岁就知道 2014-12-20 11:35:55
1954年的书，基本思想还是能看看，实在是启蒙书籍，连中位数，众数，平均数还要拿一个chapter来讲。大学本科或者现代人应该都了解。
1 有用 Yijing 2012-12-09 16:59:50
其实某些手段我自己都曾不知不觉用过
1 有用 ichbinluz 2017-05-07 23:25:49
非常适用于argument的理解。就是你说一个survey，怎么能钻牛角尖武断先假设它有不公正呢？可它就是会有非常非常多的问题，本身，任何一个survey的客观性。
0 有用 Pinkee 2009-05-16 11:41:31
还不错。对于专业人士当然简单了些。
0 有用 木風 2023-12-01 23:45:00 美国
雖然有些話顛來倒去的說，意思還是在那裡的。誰能料到八十年後的今天，還是這些問題。
0 有用 埃姆维霹 2023-06-22 15:58:00 北京
数据五问：who say so? how does he know? what's missing? did somebody change the subject? does it make sense?
0 有用 撒冷的乌凌 2023-04-04 23:50:01 美国
成书如此之早，却毫不过时。明明都讲的差不多的东西，这本书的简明有趣简直令人感动。
0 有用 章鱼喵是差分机 2023-01-13 07:39:22 德国
睡前读物，睡不着就读一下，终于读完了。挺好玩的，就是太初级了。 之前我给过一个 talk 使用不同模型得出相反结论，而且每个模型的结论都是统计上会“significant”. 还有一个工作中经常会犯的错误，就是忽略长尾分布的长尾，实验的时候模型太完美了，结果一上线疯狂亏钱。 我们有个经济学团队最近搞了一个模型，一直在吹牛说模型多好，结果最近他们发了一个模型总结，metrics 都不错，但咋一眼看... 睡前读物，睡不着就读一下，终于读完了。挺好玩的，就是太初级了。 之前我给过一个 talk 使用不同模型得出相反结论，而且每个模型的结论都是统计上会“significant”. 还有一个工作中经常会犯的错误，就是忽略长尾分布的长尾，实验的时候模型太完美了，结果一上线疯狂亏钱。 我们有个经济学团队最近搞了一个模型，一直在吹牛说模型多好，结果最近他们发了一个模型总结，metrics 都不错，但咋一眼看去预测和真值没啥关系啊，这就是另一个问题，就是故意挑好看的 metrics. 唉，有的人就是真的在 “lie with confidence” 啊。 (展开)
0 有用 pajamas走天下 2022-07-29 23:07:58
总结了一些常见的用统计学骗人的伎俩，例子有点老，不过精炼