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Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

It is true that validity is not guaranteed by performing statistical tests alone. Let's forget what we have already invested. If we had not already used the KS test, would we still have chosen an approach that does not account for effects of truncation on the meaning of distribution tails, especially when data are naturally bounded, as is the case for income or credit scores? This can significantly distort comparisons, even with very close distributions.

En réponse à@sara_sato_143

Most of this is noise; opposition is not logical but contextual.
A gating network is just a refinement mechanism, not a fundamental alternative to existing venture capital prediction methods.
These tools manage weights to improve accuracy; they do not replace base models.
For example, a Korean fund could use such a network to filter signals specific to local markets or regulatory requirements, thus complementing its existing analyses.

Most of this is noise; the debate on gating networks versus basic methods ignores fundamental logic.
These methods are in dialectical opposition and not just contextual improvements, because they differ fundamentally in their prediction approach.
A gating network dynamically evaluates data, which is an active stance, while basic methods offer a more static view.
For example, a Korean investor seeking clear signals for high-growth tech companies demands a discipline of dynamic integration of new justifications.
It is an active versus passive mechanism, not just a simple complementarity.

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.

Leo Sato

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