达莱尔·哈夫,美国统计专家。1913年出生在美国爱荷华州,毕业于爱荷华州立大学(the State University of lowa),获得学士学位和硕士学位,在此期间他由于成绩优异加入了美国大学优等生的荣誉学会(Phi Beta Kappa),同时还参加了社会心理学、统计学以及智力测验等研究项目。达莱尔·哈夫的文章多见于《哈泼斯》、《星期六邮报》、《时尚先生》以及《纽约时报》等美国顶尖媒体。1963年,由于他的贡献被授予国家学院钟奖(National School Bell )
"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
作者对“行骗”方式的归纳是: 1.谁说的? 2.他们是如何知道的? 3.遗漏了什么? 4.是否有人偷换了概念? 5.这个资料有意义吗? 我向从另一个角度来重新归纳一下这个问题: 1. 样本本身 2. 选择的数据 3. 表达形式 首先,从样本来看 第一,样本总量必须足够大时,得出的数据...
评分 评分 评分 评分书太老了,语句很怪,精髓还在那里。同等书推荐drunkard's walk
评分一本小书,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。
评分实际操作中,要在短时间内发现一个数据的无用或者欺骗性可能是件很复杂的事,虽然基本原理就那么些。
评分值得一读,数理统计说谎有两个方面:一个是操弄数据,一个是逻辑谬误。对付前者,值得一做的事是去学习数理统计基本知识,对付后者,就要加强逻辑思维能力了。
评分半个世纪前的书
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