October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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统计实验与显著性检验
｜
105
3.9.3
费希尔精确检验
对于刚刚介绍过的打乱重排后的重抽样，卡方分布是它的一种良好近似，但当计数值非常
小（只有个位数，尤其是小于等于
5
）的时候
，卡方分布就不适用了。在这种情况下，重
抽样方法得到的
p
值更
加准确。实际上，多数统计软件中有一种过程，可以穷举出
所有
可
能发生的重新排列（置换）并列出它们频数的表格，最后精确地确定观测结果的极端程
度。这种过程就称为
费希尔精确检验
，是以伟大的统计学家
R. A.
Fisher
的名字命名的。实
现基本的费希尔精确检验的
R
代码非常简单：
> fisher.test(clicks)
Fisher's Exact Test for Count Data
data: clicks
p-value = 0.4824
alternative hypothesis: two.sided
这个
p
值与使用重抽样方法得到的
p
值非常接近。
如果一些计数值很小，而另外一些很大（例如，转换率的分母），就必须进行一次重排置
换检验，而不是完全的精确检验，因为这时很难计算出所有可能的置换。前面的
R
函数
有
几个参数可以控制是否使用这种近似（
simulate.p.value=TRUE or FALSE
）、要进行多少次
迭代（
B=...
），以及在计算上的限制条件（
workspace=...
），后者可以限制为了得到
精确
结果而进行的计算程度。
在
Python
中，没有能轻易实现费希尔精确检验的方法。
检测科学欺诈
美国塔夫茨大学研究员
Thereza Imanishi-Kari
的案件是一个有趣的例子 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.