For model development, must the sample size always be less than 100%?

In the context of model development, it is not a requirement for the sample size to always be less than 100%. In fact, it is possible to utilize 100% of the available data when training a model. Using the entire dataset allows the model to learn from all available information, potentially improving its performance by providing it with more comprehensive insights.

However, while using 100% of the data may seem advantageous, it can also lead to challenges such as overfitting, where the model learns the noise in the data instead of generalizing to unseen data. Therefore, it is often a best practice to reserve a portion of the data for validation and testing purposes. This approach helps ensure that the model can perform well on new, unseen data, striking a balance between training effectiveness and generalization capability.

Thus, saying that the sample size must always be less than 100% is not accurate, as effective model training can occur with the full dataset, provided that appropriate validation techniques are implemented.