Interpret Kolmogorov-Smirnov Test

Hi ,
I want to know, how to statistically confirm where this data is normally distributed or not.
Here is the data

x = abs(round(rnorm(2000, mean = 55, sd= 5)))
ks.test(x, 'pnorm')

Histogram and qq plot shows data is normally distributed .
ks test shows less than 0.05 pvalue

> ks.test(x, 'pnorm')

	One-sample Kolmogorov-Smirnov test

data:  x
D = 1, p-value < 2.2e-16
alternative hypothesis: two-sided

Is this the correct way to use ks.test . if not what are the other statistical tests i can use to confirm the normality of data.



you should use something like Shapiro Wilk or Lillieforse Test to test for normality. The Shapiro Wilk test is done in R with shapiro.test() and the Lillieforse Test can be imported from nortest::lillie.test(). The advantages are from a statistical point of view, but in general you should avoid KS-Test, especially if you are working with parameter estimates from a sample (if you know the true parameters of the population, you can use KS to confirm the sample is from the known population, but everything else should be done without KS test).

Kind regards

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