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
十个小朋友分苹果,分别拿到1、2、3、4、5、5、10、10、10、100,那么平均每个小朋友分到几个苹果?可能大家都会说是15个,十组数据加起来除以10就能算出15;我说是5个,因为5是这组数据的中位数,即一半数据比5大一般数据比5小;我还可以说是10个,因为10是这组数据的众数,它...
评分 评分作者对“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...
评分前段时间看到一份数据,说中国人均存款是7万多。新浪微博做了一个热点话题,问“你拖后腿了吗”?如果新浪多点节操,这个话题的相关问题应该是:“你又被平均数据忽悠了吗?” 互联网带来的信息剧增给我们处理信息的能力提出了新的要求。尤其在中国,太多中国人缺乏批...
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。
评分一本薄薄的小书,是看编程珠玑的某一页提到的推荐。找来原版pdf打印出来花了不到一周时间读完了,主要列举了日常生活中常见的用统计数据进行欺骗的方法~这本书好像还有中译本(《统计陷阱》),然而翻译就差强人意了,而且很多地方有意略去不翻…例如第九章关于马克思剩余价值那部分…呵呵哒~
评分非常适用于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。
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。
评分作为<asking the right questions>里<can the statistics be deceptive?>那章的补充~
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