What type of distribution does the Kolmogorov-Smirnov test examine?
The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2). The test is nonparametric. It does not assume that data are sampled from Gaussian distributions (or any other defined distributions).
What is a good K-S score?
K-S should be a high value (Max =1.0) when the fit is good and a low value (Min = 0.0) when the fit is not good. When the K-S value goes below 0.05, you will be informed that the Lack of fit is significant.
How do you use Kolmogorov-Smirnov?
General Steps
- Create an EDF for your sample data (see Empirical Distribution Function for steps),
- Specify a parent distribution (i.e. one that you want to compare your EDF to),
- Graph the two distributions together.
- Measure the greatest vertical distance between the two graphs.
- Calculate the test statistic.
How does Kolmogorov-Smirnov work?
The Kolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used to decide if a sample comes from a population with a specific distribution. where n(i) is the number of points less than Yi and the Yi are ordered from smallest to largest value.
What is Kolmogorov-Smirnov and Shapiro-Wilk tests?
Briefly stated, the Shapiro-Wilk test is a specific test for normality, whereas the method used by Kolmogorov-Smirnov test is more general, but less powerful (meaning it correctly rejects the null hypothesis of normality less often).
How do you read KS test results?
The p-value returned by the k-s test has the same interpretation as other p-values. You reject the null hypothesis that the two samples were drawn from the same distribution if the p-value is less than your significance level.