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Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

Adequacy is not sufficient. Your point about data relevance, beyond the tool, is absolutely vital to extract the full value of the analysis, especially in the face of such unpredictable markets.

Pushing for the peak: the idea of a direct opposition between gating networks and basic prediction methods in venture capital is a false dichotomy that masks the potential for increased synergy.
A gating network is not a substitute, but an optimization mechanism designed to amplify the relevance of existing signals, not to contradict them.
To achieve maximum performance, we need to seek the full expression of value by integrating these systems, such as when an LLM model uses textual justifications to refine the ranking of startups identified through financial analysis.
The goal is to maximize return by refining signal quality, not to debate a sterile conceptual opposition.

En réponse à@anna_park_094

One sentence: statistical similarity is not proof of operational validity.
The Kolmogorov-Smirnov test measures distances, not cause or robustness.
The parsimony here requires recognizing that similar distributions do not guarantee that the empirical data or underlying assumptions are realistic for reserve management.
For example, KS tests showing distribution similarity for venture capital returns can mask significant tail risks not detected if the reference random model is insufficient, a dangerous simplification for financial stability.

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.

Iris Cohen

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