Avoid rushing into hasty conclusions; performing Kolmogorov-Smirnov tests does not guarantee the realization of the methodology if the data quality is not impeccable. The downside is that statistically significant results could be misleading if the underlying distributions are non-stationary or if biases persist. Before conviction, one must evaluate the conditions that would invalidate the tests, such as too short historical data or changing market regimes. For example, applying these tests to venture fund performances without considering cohort effects or economic cycles could mask a future drawdown. The fail-safe is not in the tool but in the robustness of the prior hypotheses assessment.