The Kolmogorov-Smirnov test ( KS-test) is one of the useful and general nonparametric method for comparing two samples. It can be used to test whether the two samples are different in the location and the shape of empirical distribution functions. As a nonparametric test, it does not require the normality of the population.
The KS-test is based on the empirical distribution function. From the empirical distribution function. We can see the difference from the two samples. And then determine whether to reject the null hypothesis or not.
The missing values in the data range will be excluded in the analysis
From Origin 2015, missing values in the grouping range and the corresponding data values will be excluded in analysis. In the previous version, missing values in the grouping range will be considered as a group.
To perform a one-sample a two-sample Kolmogorov-Smimov test:
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