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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88
｜
第
3
章
3.4.1
p
值
只看图形，这不是一种非常精确的测量统计显著性的方法。更好的方法是使用
p
值
。
p
值
是随机模型得到比观测结果更极端的结果的频率。我们可以根据置换检验估计出
p
值，
算
出置换检验得到大于等于已观测差异的差异次数比例即可：
mean(perm_diffs > obs_pct_diff)
[1] 0.308
np.mean([diff > obs_pct_diff for diff in perm_diffs])
在这里，
R
和
Python
都用
1
表示真，用
0
表示假。
p
值是
0.308
，这说明我们能够期望以超过
30%
的随机概率
，得到和观测结果一样极端或
更加极端的结果。
在这个例子中，我们不使用置换检验也能得到
p
值
。因为有一个二项分布，所以可以近似
地算出
p
值。在
R
代码中，可以使用函数
prop.test
来完成这个任务：
> 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 ...
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