Did you know that baseball players whose names begin with the letter “D” are more likely to die young? Or that Asian Americans are most susceptible to heart attacks on the fourth day of the month? Or that drinking a full pot of coffee every morning will add years to your life, but one cup a day increases the risk of pancreatic cancer? All of these “facts” have been argued with a straight face by credentialed researchers and backed up with reams of data and convincing statistics.
As Nobel Prize–winning economist Ronald Coase once cynically observed, “If you torture data long enough, it will confess.” Lying with statistics is a time-honored con. In Standard Deviations, economics professor Gary Smith walks us through the various tricks and traps that people use to back up their own crackpot theories. Sometimes, the unscrupulous deliberately try to mislead us. Other times, the well-intentioned are blissfully unaware of the mischief they are committing. Today, data is so plentiful that researchers spend precious little time distinguishing between good, meaningful indicators and total rubbish. Not only do others use data to fool us, we fool ourselves.
With the breakout success of Nate Silver’s The Signal and the Noise, the once humdrum subject of statistics has never been hotter. Drawing on breakthrough research in behavioral economics by luminaries like Daniel Kahneman and Dan Ariely and taking to task some of the conclusions of Freakonomics author Steven D. Levitt, Standard Deviations demystifies the science behind statistics and makes it easy to spot the fraud all around.
Gary Smith is the Fletcher Jones Professor of Economics at Pomona College in Claremont, California. He received his Ph.D. in Economics from Yale University and taught there as Assistant Professor for seven years. He has won two teaching awards and authored more than seventy academic papers, nine textbooks, and seven educational software programs. This is his first trade book.
第二本啃完的统计学书籍 比起第一本讲贝叶斯理论的这本专著于统计学中的逻辑陷阱,但我真的不知道为什么市面上的统计学书籍都执着于把自己命名为“极简”“生活中的”,让人看起来好像没那么硬敢于翻开第一页(但其实还是很硬)。特别有意思的是我同期看小岛宽之和加里 史密斯...
评分以前偶然看到有个豆瓣网友写道“第一次学统计学,感觉这是上帝的密码”,当时对这句话不以为然,直到看了这本书,虽然比较浅显易懂,但是仍然隐约带给了我巨大的震撼。统计学绝对是一门博大精深且充满魅力的学科,我想以后有机会还会购买相对专业和系统的图书进行阅读。 谈谈这...
评分首先想好你的观点:初中生都爱吃棒棒糖! 然后用数据证明你的观点。 不好!数据好像反对了我的观点。 那一定是选择数据的时间错误了。减一年。改为:初一到初二的初中生都爱吃棒棒糖! 还是不行。 那一定是选择数据的对象错误了,我的观点只针对女性。改为:初一到初二的女初中...
评分人类由于两种倾向而导致认知错误: 1容易被模式、解释模式的理论所引诱。 2总有寻找支持假设的证据而忽视或曲解与假设相反的证据的倾向。 纠正这两种倾向所引起的认知错误之方法: 1常识。不寻常的说法应该有压倒性的证据支持。 2新数据。在旧数据已经支持理论的时候,再用旧数...
评分整本书,差不多就是大型的学术打假?基本就是把各种人吊着打一遍,看得还算过瘾 虽然书分了很多章节,不过作者想表达的点,大概主要就是以下几个: 1、模式:不要轻易相信数据中的模式。随机数也会显示出某些模式。这种模式是无意义的 2、统计显著性:只要你愿意,总能找到数据...
一直不喜欢励志、how to(包括某些所谓管理学图书)、技术分析、特异功能,这本书说出了其所以然。
评分一直不喜欢励志、how to(包括某些所谓管理学图书)、技术分析、特异功能,这本书说出了其所以然。
评分一直不喜欢励志、how to(包括某些所谓管理学图书)、技术分析、特异功能,这本书说出了其所以然。
评分一直不喜欢励志、how to(包括某些所谓管理学图书)、技术分析、特异功能,这本书说出了其所以然。
评分一直不喜欢励志、how to(包括某些所谓管理学图书)、技术分析、特异功能,这本书说出了其所以然。
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