Pushing for the top: the idea that executing Kolmogorov-Smirnov tests alone realizes the distributional testing methodology is a dangerous simplification.
The validity of this realization entirely depends on the robustness of the underlying assumptions and the quality of the reference data.
If the data used for comparison do not reflect the fluctuating realities of markets, such as oil reserves or petrodollar liquidity, the results, even with minor deviations, can be misleading.
For example, comparing empirical data from emerging markets with benchmarks established on stable economies could hide crucial asymmetries, making the statistical similarity a false assurance.