python - Kolmogorov-Smirnov test in Scipy with non-normalized data -

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I am trying to check whether a list of values ​​is evenly distributed or not. I know that there is a proper examination to run the Kolmogorov-Smirnov test. However, my results do not matter to me.

In the code given below, I create two lists of values ​​that are evenly distributed, and y is not evenly distributed.

What should be: I run SippyCest () on both XP and Y, and for the X pass does not pass P-Well, YK-P-value. / P>

What's happening: I run SippyCest () on XP and Y, and P-Value is 0.0 for both X and Y. [1] In SPE import data, SP imports are imported as SP imports, as imported matplotlib.pyplot plt% matplotlib inline according to NP import math import [2]: X = np.random.uniform (size = 1000) sigma_x = np .std (x) mean_x = x.mean () plt.hist (x) plt.show () in [3]: y = x ** 4 sigma_y = Np.std (y) mean_y = y.mean () plt [4]: ​​(0.499, 0.0) in [5]: statistics. (4):. Kstest (y, 'uniform', args = (mean_y, sigma_y)) [5]: (0.67400000000000004, 0.0)

You are abusing the argument . This is not always the desired mean and standard deviation, this is the logic that the distribution you are using He takes it. In this situation, two logic takes loc and scale , and between " loc and loc + scale

So you do not want to use mean and standard deviation instead you want to define a minimum and more similar definition, args = (0, 1) If you want to test against a uniform equal distribution, or args = (min (X), max (x)) if you want to use sample estimates. Import import data from 

  npx = np.random.uniform (size = 1000) Y = x ** 4 stats. Kestest (X, 'Uniform', Args = (0, 1)) # (0.02953849 9688200326, 0.34247911001793319) Statistics. Kestest (y, 'uniform', args = c (0, 1)) # (0.50121963249814794, 0.0)

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