Understanding the Kolmogorov-Smirnov Test in Statistics
The Kolmogorov-Smirnov test is a powerful non-parametric statistical test used to determine whether a given sample departs from a hypothesized probability distribution. Unlike the Chi-Square test, the K-S test does not require data binning, making it exceptionally useful for continuous datasets.
Key Advantages
One of the primary benefits of the K-S test is its distribution-free nature regarding the cumulative distribution function. It directly evaluates the maximum vertical deviation between empirical observations and theoretical expectations, providing robust diagnostic power even with moderate sample sizes.