Finding Two-Tailed P Value from t-distribution and Degrees of Freedom in Python
p-value, python, scipy, statistics
Solution
Yes, `n-1` is the degrees of freedom in that example.
Given a t-value and a degrees of freedom, you can use the "survival function" `sf` of `scipy.stats.t` (aka the complementary CDF) to compute the one-sided p-value. The first argument is the T value, and the second is the degrees of freedom.
For example, the first entry of the table on this page says that for 1 degree of freedom, the critical T value for p=0.1 is 3.078. Here's how you can verify that with `t.sf`:
In [7]: from scipy.stats import t
In [8]: t.sf(3.078, 1)
Out[8]: 0.09999038172554342 # Approximately 0.1, as expected.
For the two-sided p-value, just double the one-sided p-value.
Problem
How do I determine the P Value of a t-distrobution with n degrees of freedom. Research on this subject points me to this stack exchange answer: https://stackoverflow.com/a/17604216 I assume np.abs(tt) is the T-value, but how do i work in degrees of freedom, is that the n-1? Thanks in advance