# How to choose parameters for Kolmogorov-Smirnov test in python?

I am testing the set of data of coefficient J. It looks like this I want to test the normality of this set using the K-S test, but I.m not sure how the parameters should be adjusted here. I wrote

from scipy.stats import kstest
seed(1234)
# normality test
result = kstest(J_ridge.flatten(), 'norm',alternative='greater')
>> KstestResult(statistic=0.49068097451020654, pvalue=0.0)


But I don't know what alternative to use or other parameters needed in the K-S test.

• Hi there, welcome to the site! It's worth thinking about why exactly you need a test of normality. You could take a look at the answers here for more context: stats.stackexchange.com/questions/2492/… Aug 31, 2021 at 12:41

Perhaps a more useful test of normality, if $$\mu$$ and $$\sigma$$ are unknown, would be the Shapiro-Wilk test.

The null hypothesis of the Kolmogorov-Smirnov test is that the population from which data are sampled has a specific normal distribution (with specified mean $$\mu$$ and and standard deviation $$\sigma.)$$ Consequently, if you used a K-S test, you would need to estimate $$\mu \approx \bar X = \frac{1}{n}\sum_{i=1}^n X_i$$ and $$\sigma^2 \approx S^2= \frac{1}{n-1}\sum_{i=1}^n (X_i-\bar X)^2,$$ but you would have to allow for that estimation in determining the P-value of the K-S test.

By contrast, the null hypothesis of the Shapiro-Wilk test is that the population from which data are randomly sampled is some normal distribution (with unspecified parameters). Another advantage is that the S-W test has better power (is more likely to detect actual non-normality) for a given sample size.

Example in R:

Sample of size $$n=500$$ from $$\mathsf{Norm}(\mu=100, \sigma=10):$$

set.seed(831)
x = rnorm(500, 100, 10)
summary(x);  length(x);  sd(x)
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
73.63   94.31  100.08  100.30  106.79  127.85
 500        # sample size
 9.575485   # sample standard deviation


The null hypothesis is not rejected at the 5% level; P-value 0.674. So data $$X$$ are 'consistent with normal'.

shapiro.test(x)

Shapiro-Wilk normality test

data:  x
W = 0.99754, p-value = 0.674


Linear transformation of $$X$$ changes parameters, but $$Y$$ still passes the normality test (P-value unchanged).

y = .5*x + 2
summary(y);  length(y);  sd(y)
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
38.81   49.16   52.04   52.15   55.39   65.93
 500
 4.787742
shapiro.test(y)$p.val  0.6739557  Nonlinear transformation, destroys normality. S-W test rejects normality for $$W$$ (P-value below 5%). summary(w); length(w); sd(w) Min. 1st Qu. Median Mean 3rd Qu. Max. 5421 8895 10016 10151 11404 16347  500  1935.915 shapiro.test(w)$p.value
 0.001056042


Graphical displays:

The histogram of $$W$$ is right-skewed, not normal. The normal quantile plot of $$W$$ (right panel) is distinctly nonlinear. 