Kolmogorov-Smirnov test
Q. What is Kolmogorov-Smirnov test used for?
Used to assess whether a sample follows a specified distribution or to compare two samples’ distributions.
Q. What is the difference between T test and Kolmogorov-Smirnov test?
T-test compares means of two groups; KS test compares entire distributions for similarity or goodness-of-fit.
Q. How do you interpret Kolmogorov-Smirnov test for normality?
If p-value is high (e.g., > 0.05), data may follow normal distribution; low p-value suggests departure.
Q. How do you interpret KS test p value?
If the p-value is below the chosen significance level (commonly 0.05), we would reject the null hypothesis. It indicates significant difference; large p-value (i.e.below the chosen significance level ) suggests no significant difference.
Q. Which normality test is best?
No one-size-fits-all. Anderson-Darling, Shapiro-Wilk, and KS test are commonly used; choice depends on data size and characteristics.
Kolmogorov-Smirnov Test (KS Test)
The Kolmogorov-Smirnov (KS) test is a non-parametric method for comparing distributions, essential for various applications in diverse fields.
In this article, we will look at the non-parametric test which can be used to determine whether the shape of the two distributions is the same or not.