The following test for a difference between the centre of a sample of data and a given reference point. The one sample t-test assumes normally distributed data, whereas the Wilcoxon signed rank test can be used with any data.
One sample t-test
import numpy as np import scipy.stats as stats # generate the data normDist = stats.norm(loc=7.5, scale=3) data = normDist.rvs(100) # Define a value to check against checkVal = 6.5 # T-test # --- >>> START stats <<< --- t, tProb = stats.ttest_1samp(data, checkVal) # --- >>> STOP stats <<< --- print ('P value:') print (tProb) OUT: P value: 0.007269398564245046
Wilcoxon signed rank test
Note the test value is subtracted from all data (test is then effectively against zero).
rank, pVal = stats.wilcoxon(data-checkVal) print ('P value:') print (pVal) OUT: P value: 0.006010251964988403