Python中t检验的置信区间(均值之间的差异) [英] Confidence Interval for t-test (difference between means) in Python

问题描述

我正在寻找一种快速的方法来获取Python中t检验的置信区间，以求出均值之间的差异.类似于R:

I am looking for a quick way to get the t-test confidence interval in Python for the difference between means. Similar to this in R:

X1 <- rnorm(n = 10, mean = 50, sd = 10)
X2 <- rnorm(n = 200, mean = 35, sd = 14)
# the scenario is similar to my data

t_res <- t.test(X1, X2, alternative = 'two.sided', var.equal = FALSE)    
t_res

出局:

    Welch Two Sample t-test

data:  X1 and X2
t = 1.6585, df = 10.036, p-value = 0.1281
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.539749 17.355816
sample estimates:
mean of x mean of y 
 43.20514  35.79711 

下一步:

>> print(c(t_res$conf.int[1], t_res$conf.int[2]))
[1] -2.539749 17.355816

考虑到有意义区间在假设检验中的重要性(以及最近报告p值的实践受到多少批评)，我在统计模型或scipy中都没有发现类似的东西，这很奇怪. >

I am not really finding anything similar in either statsmodels or scipy, which is strange, considering the importance of significance intervals in hypothesis testing (and how much criticism the practice of reporting only the p-values recently got).

推荐答案

此处介绍如何使用StatsModels的

Here how to use StatsModels' CompareMeans to calculate the confidence interval for the difference between means:

import numpy as np, statsmodels.stats.api as sms

X1, X2 = np.arange(10,21), np.arange(20,26.5,.5)

cm = sms.CompareMeans(sms.DescrStatsW(X1), sms.DescrStatsW(X2))
print cm.tconfint_diff(usevar='unequal')

输出为

(-10.414599391793885, -5.5854006082061138)

并匹配R:

> X1 <- seq(10,20)
> X2 <- seq(20,26,.5)
> t.test(X1, X2)

    Welch Two Sample t-test

data:  X1 and X2
t = -7.0391, df = 15.58, p-value = 3.247e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -10.414599  -5.585401
sample estimates:
mean of x mean of y 
       15        23 

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