October 2018
Beginner to intermediate
238 pages
6h 32m
Chinese
Content preview from 面向数据科学家的实用统计学
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统计实验与显著性检验
｜
75
在本例中，我们不需要使用置换检验也可以获得
p
值。根据二项分布，我们可以使用正态
分布近似估计
p
值。在使用
R
语言编程时，函数
prop.test 执行该操作。
> prop.test(x=c(200,182), n=c(23739,22588), alternative="greater")
2-sample test for equality of proportions with continuity correction
data: c(200, 182) out of c(23739, 22588)
X-squared = 0.14893, df = 1, p-value = 0.3498
alternative hypothesis: greater
95 percent confidence interval:
-0.001057439 1.000000000
sample estimates:
prop 1 prop 2
0.008424955 0.008057376
在函数的输出中，参数 x 表示各组的成功次数，参数 n 是试验次数。我们可以看到，由正
态近似所生成的
p
值为
0.3498
，接近于使用置换检验所得到的
p
值。
3.4.2
α
值
完全根据研究人员的判断力去确定一个结果是否“太不寻常”因而是偶然的，统计学家无
疑会对此做法大皱眉头。在统计学家看来，正确的做法是提前设定一个阈值，例如“超过
随机（零假设）结果
5%
”。这样的阈值被称为
α
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