Parsimony in model building emphasizes:

Parsimony in model building emphasizes:

• A. A measure of the complexity of a model.
• B. A rule to transform variables to achieve normality.
• C. A method for maximizing the number of predictors.
• D. Keeping models as simple as possible by avoiding unnecessary variables.

Answer: (D)

Parsimony is about keeping a model simple while still explaining the data well. The idea is to include predictors only if they meaningfully improve the model’s fit and to drop unnecessary variables, helping to avoid overfitting and improving how well the model generalizes to new data. That’s why keeping models as simple as possible by avoiding unnecessary variables is the best description. It isn’t about transforming variables to achieve normality, nor is it a rule to maximize the number of predictors. It also isn’t simply a measure of complexity itself; it’s the principle of prioritizing simplicity without sacrificing adequacy.

Parsimony is about keeping a model simple while still explaining the data well. The idea is to include predictors only if they meaningfully improve the model’s fit and to drop unnecessary variables, helping to avoid overfitting and improving how well the model generalizes to new data. That’s why keeping models as simple as possible by avoiding unnecessary variables is the best description.

It isn’t about transforming variables to achieve normality, nor is it a rule to maximize the number of predictors. It also isn’t simply a measure of complexity itself; it’s the principle of prioritizing simplicity without sacrificing adequacy.