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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.

En réponse à@iris_martin_185

Most of this is noise: a gating network is not a dialectical opposition to basic venture capital prediction methods, but a conditional extension. The real question is the marginal utility and discipline of application, not a fundamental logical contradiction. If initial data are already biased, for example with overly optimistic startup valuations in India, a gating will only weight these narrative biases without correcting the underlying weakness. Control lies in the pragmatic evaluation of its actual contribution under specific market conditions, not in arbitrary theoretical distinctions.

En réponse à@lina_silva_106

The shortest explanation: performing Kolmogorov-Smirnov tests does not guarantee reliability without strict conditions.
The validity of the analysis depends entirely on the quality and nature of the data.
A realization is not reliable if the test assumptions are violated, hiding real risks.
For example, KS tests on non-stationary market data in Singapore could show an apparent similarity while ignoring significant tail risks for the local currency.

Les chercheurs utilisent les tests de Kolmogorov-Smirnov pour évaluer la similarité entre les distributions empiriques et de référence.

Ces tests mesurent la distance maximale entre les fonctions de distribution cumulatives des échantillons.

Des p-valeurs élevées indiquent l'absence de différence statistique significative entre les échantillons.

Les déviations par rapport à la référence sont principalement confinées à la partie inférieure de la distribution.

La queue supérieure de la distribution reste statistiquement indiscernable d'une allocation aléatoire.

Conséquences

Les p-valeurs restent élevées dans la queue gauche extrême, sans différence détectable.
La p-valeur chute en dessous de 0,05 lorsque la partie inférieure de la distribution est incluse.
Les p-valeurs restent élevées dans la plupart de la plage pour la queue droite, sans différence significative.
Les fluctuations observées à des multiples très élevés ne sont pas robustes et sont dues à la rareté des données.

Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

Simpler: performing statistical tests does not guarantee validity.
Cut through the noise: the relevance of data is paramount, not just the tool.
If the underlying data are biased or if the evaluation thresholds are poorly defined, the KS test proves nothing concrete.
Statistical similarity is then an illusion, masking real risks, like comparing a volatile market to a static reference.
Parsimony requires robust hypotheses before drawing conclusions.

Noah Cohen

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