MODEL VALIDATION
Challenge the result before relying on it.
Model validation examines assumptions, data integrity, reproducibility, robustness and the conditions under which an analytical result may fail.
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Make the model reviewable.
A model should have a documented purpose, defined inputs and an explanation of its principal assumptions. Review begins by understanding what the model is intended to do and what lies outside its scope.
This foundation makes it possible to assess a result without relying solely on the confidence of its developer.
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Reproduce and challenge.
Reproducibility checks whether the stated method produces the stated result. Challenge asks whether alternative explanations, data choices or parameter settings materially change the interpretation.
Both are necessary. A reproducible result may still rest on assumptions that are too narrow for the intended use.
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Examine adverse conditions.
Validation considers how a model behaves when inputs are noisy, relationships change or market conditions differ from those used in development. Stress scenarios can expose dependencies that are not obvious in an average-case analysis.
The objective is to understand the boundary of the model’s usefulness.
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Document the conclusion and its limits.
A validation conclusion should describe the evidence reviewed, the limitations identified and the conditions requiring further monitoring or reassessment.
Model approval, where applicable, should not be interpreted as a claim that the model will remain valid in all future conditions.
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