Which option describes parsimony in model selection?

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Multiple Choice

Which option describes parsimony in model selection?

Parsimony in model selection means keeping explanations simple and avoiding unnecessary complexity. The best description captures the idea of favoring the simplest model that still fits the data, because additional complexity can fit noise rather than true signal. This approach helps models generalize better to new data and guards against overfitting. In practice, we balance fit with simplicity by using criteria that penalize extra parameters or complexity. The other ideas—pushing for the most complex model, relying on popularity, or choosing arbitrarily—do not follow this principled preference for simplicity that still explains the data.

Parsimony in model selection means keeping explanations simple and avoiding unnecessary complexity. The best description captures the idea of favoring the simplest model that still fits the data, because additional complexity can fit noise rather than true signal. This approach helps models generalize better to new data and guards against overfitting. In practice, we balance fit with simplicity by using criteria that penalize extra parameters or complexity. The other ideas—pushing for the most complex model, relying on popularity, or choosing arbitrarily—do not follow this principled preference for simplicity that still explains the data.

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