Simply put, a statistical test does not create statistical similarity; it only measures it.
The realization of similarity depends on the underlying hypotheses and the relevance of the reference data.
If the benchmark used does not represent reality, like a normal distribution for venture capital returns, the KS test is misleading.
This parsimony in analysis is crucial; a fundamental error in the reference model invalidates any conclusion, regardless of the accuracy of the calculation.
A KS test claiming the similarity of venture capital returns to a normal distribution, for example, ignores tail risks and is not proof of similarity.