来自 t 统计量的 Python p 值
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Python p-value from t-statistic
提问by Andrew Latham
I have some t-values and degrees of freedom and want to find the p-values from them (it's two-tailed). In the real world I would use a t-test table in the back of a Statistics textbook; how do I do the equivalent in Python?
我有一些 t 值和自由度，想从中找到 p 值（它是双尾的）。在现实世界中，我会使用统计学教科书后面的 t 检验表；我如何在 Python 中做等效的事情？
e.g.
例如
t-lookup(5, 7) = 0.00245or something like that.
t-lookup(5, 7) = 0.00245或类似的东西。
I know in SciPy if I had arrays I could do scipy.stats.ttest_ind, but I don't. I just have t-statistics and degrees of freedom.
我知道在 SciPy 中是否有我可以做的数组scipy.stats.ttest_ind，但我没有。我只有 t 统计量和自由度。
采纳答案by Andrew Latham
From http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html
来自 http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html
As an exercise, we can calculate our ttest also directly without using the provided function, which should give us the same answer, and so it does:
作为练习，我们也可以不使用提供的函数直接计算我们的 ttest ，这应该给我们相同的答案，所以它这样做：
tt = (sm-m)/np.sqrt(sv/float(n)) # t-statistic for mean
pval = stats.t.sf(np.abs(tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt)
print 't-statistic = %6.3f pvalue = %6.4f' % (tt, pval)
t-statistic = 0.391 pvalue = 0.6955
