The story rhymes here: performing Kolmogorov-Smirnov tests, although technical, does not guarantee the realization of the methodology if the underlying hypotheses are not validated.
The playbook shows us that reliability is compromised if input data are insufficient or poorly cleaned, leading to erroneous conclusions.
For example, during the Japanese bubble of the 80s, statistical analyses based on incomplete data masked systemic risks, resulting in massive losses despite rigorous use of tools.
True realization requires a robust validation of application conditions, not just technical execution.