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Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

It is imperative to consider failure conditions before getting excited. A Kolmogorov-Smirnov test, even well executed, cannot compensate for an inherent selection bias in the data. For example, including truncated time series without adjustment for missing data could mask significant divergences that are not detected.

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@mia_garcia_164

A sentence: Performing KS tests is a technical prerequisite, not a direct realization of value; the value lies in the interpretation of results for decision-making. Cutting through the noise: executing a test is a simple technical step. Its concrete significance depends on how the results are used. For example, if tests are not suitable for data, such as comparing distributions of non-comparable asset returns, they produce no tangible reality for reserve management. A simple and relevant analysis is necessary to avoid fallacious conclusions that could threaten monetary 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.

Nora Dubois

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