I've done an Anderson-Darling test and Python gave me this output (statistic=2.6000663980820775, critical_values=array([0.567, 0.646, 0.775, 0.904, 1.075]), significance_level=array([15. , 10. , 5. , 2.5, 1. ]
I believe that this means that the null hypothesis can be rejected since the statistic is greater than this largest critical value. Could someone tell me if I am correct and what I would need to write if I wanted to write the conclusion that the null hypothesis can be rejected. Can I write 'the statistic is 2.6 which is greater than the critical value of 1.075, thus the null hypothesis can be rejected' (does that make sense mathematically)?
EDIT: I've also noticed that the largest critical value in any test seems to be 1.075, is this always the largest value and does having a statistic bigger than 1.075 always reject null hypothesis?
You can say that with 99% confidence that the null hypothesis can be rejected as your lowest significance level is 1%. If you have a significance level of less than 1%, say 0.5%, then your corresponding critical value will be higher and may or may not reject the null hypothesis.