I think I am dealing with this issue http://www.r-bloggers.com/normality-tests-don%E2%80%99t-do-what-you-think-they-do/
I have large data set(10k data points) that slightly diverges from normal, and I get p-value of 0. I am interested in having perhaps a more crude test that tells me if data is extremely divergent from normal, versus looking somewhat normally distributed. I am currently trying Kolmogorov-Smirnoff, and in both cases I just get p-value of 0. Any alternatives?
I also looked at this: Is normality testing 'essentially useless'?
So, taking all this into consideration, is there any kind of test I can perform that distinguishes between data that's roughly normal, and not normally distributed at all?
I am using scipy.