Modeldependent

Modeldependent is a term used in various disciplines to denote that a quantity, conclusion, or result depends on a specific model or theoretical framework. It contrasts with modelindependent results, which remain valid across a broad class of models or assumptions. The designation signals that the inference is not universal but contingent on the chosen model.

In statistics and data analysis, estimates such as effects, parameters, or predictions are often modeldependent because they assume particular stochastic processes, distributions, or functional forms (for example, linear regression assumes linearity and Gaussian errors).

In physics, experimental results often require a model to convert measurements into physical quantities; cross sections, coupling constants, or luminosities can be modeldependent because their extraction relies on assumptions about backgrounds, detector response, or signal shapes.

In machine learning, predictions and error estimates are modeldependent, shaped by architecture, training data, regularization, and optimization.

To mitigate modeldependence, researchers perform sensitivity analyses across multiple models, report model-averaged estimates, or provide model-independent bounds where possible. Transparent documentation of the modeling choices and assumptions is standard practice.