One-Sample KS Test vs Two-Sample KS Test
One-Sample KS Test | Two-Sample KS Test |
---|---|
Employed to assess whether a single sample of data conforms to a specific theoretical distribution. | Utilized to evaluate whether two independent samples originate from the same underlying distribution. |
Compares the (EDF) of the sample with the (CDF) of the theoretical distribution. | It compares the EDF of one sample with the EDF of the other sample. |
Null hypothesis assumes that the sample is drawn from the specified distribution. | Null hypothesis posits that the two samples are drawn from identical distributions. |
Test statistic, represents the maximum vertical deviation between the EDF and CDF. | The test statistic, reflects the maximum difference between the two EDFs. |
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.