Before any conviction, it is essential to consider the downside; executing Kolmogorov-Smirnov tests does not guarantee that the interpretation is an irrefutable proof.
The validity of conclusions remains conditional on the quality of the underlying data and the assumptions made.
If empirical distributions come from highly volatile markets or unrepresentative data, the similarity can be deceptive.
For example, comparing venture capital returns to random distributions does not prove competence if the random assumptions are unrealistic or if minor deviations hide significant tail risks.
Such an approach would not pass the prudence mandate of BaFin.