如何使用 numpy/scipy 执行两样本单尾 t 检验 [英] How to perform two-sample one-tailed t-test with numpy/scipy

问题描述

在R中，可以简单地使用

> A = c(0.19826790, 1.36836629, 1.37950911, 1.46951540, 1.48197798, 0.07532846)
> B = c(0.6383447, 0.5271385, 1.7721380, 1.7817880)
> t.test(A, B, alternative="greater")

    Welch Two Sample t-test

data:  A and B 
t = -0.4189, df = 6.409, p-value = 0.6555
alternative hypothesis: true difference in means is greater than 0 
95 percent confidence interval:
 -1.029916       Inf 
sample estimates:
mean of x mean of y 
0.9954942 1.1798523 

在 Python 世界中，scipy 提供了类似的功能 ttest_ind，但它只能做双尾 t 检验.我发现的关于该主题的最近信息是 this 链接，但它似乎是对 scipy 中实现单尾与双尾策略的讨论.

In Python world, scipy provides similar function ttest_ind, but which can only do two-tailed t-tests. Closest information on the topic I found is this link, but it seems to be rather a discussion of the policy of implementing one-tailed vs two-tailed in scipy.

因此，我的问题是，有没有人知道有关如何使用 numpy/scipy 执行单尾版本测试的任何示例或说明?

Therefore, my question is that does anyone know any examples or instructions on how to perform one-tailed version of the test using numpy/scipy?

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因为单面测试可以从双面退出测试.(对于对称分布，一侧 p 值仅为一半两侧 pvalue)

because the one-sided tests can be backed out from the two-sided tests. (With symmetric distributions one-sided p-value is just half of the two-sided pvalue)

接着说 scipy 总是给出带符号的测试统计量.这意味着给定来自双尾检验的 p 和 t 值，当 p/2 <;α 和 t >0，当 p/2 <α 和 t <0.

It goes on to say that scipy always gives the test statistic as signed. This means that given p and t values from a two-tailed test, you would reject the null hypothesis of a greater-than test when p/2 < alpha and t > 0, and of a less-than test when p/2 < alpha and t < 0.

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