When use Kolmogorov-Smirnov Test?
The main idea behind using this Kolmogorov-Smirnov Test is to check whether the two samples that we are dealing with follow the same type of distribution or if the shape of the distribution is the same or not.
Let’s a breakdown the scenarios where this test can be applicable:
- Comparison of Probability Distributions: The test is used to evaluate whether two samples exhibit the same probability distribution.
- Compare the shape of the distributions: If we assume that the shapes or probability distributions of the two samples are similar, the test assesses the maximum absolute difference between the cumulative probability distributions of the two functions.
- Check Distributional Differences: The test quantifies the maximum difference between the cumulative probability distributions, and a higher value indicates greater dissimilarity in the shape of the distributions.
- Hypothesis Testing Types:The assessment of the shape of sample data is typically done through hypothesis testing, which can be categorized into two types:
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.