What is the main advantage of using separate sampling?

The main advantage of using separate sampling lies in its ability to reduce the number of cases required for model building while maintaining a high level of quality in the results. When separate sampling is employed, it allows the modeler to capture enough variability within the data to build a robust model without needing to use the entire dataset. This approach is especially beneficial when dealing with large datasets, as the computational burden can be significantly decreased, leading to faster modeling processes.

By carefully selecting subsets of data that represent the overall population, the model can be trained on these samples without sacrificing accuracy or reliability. This method ensures that the essential characteristics of the data are captured, allowing for effective generalization when the model is applied to unseen data.

Larger sample sizes are often assumed to yield more reliable results, but this can come at the cost of increased processing time. The advantage of separate sampling is that it strategically balances the need for sufficient data with efficiency, ensuring that model development is both effective and efficient. In contrast, options that imply complete datasets or larger sample sizes without focusing on quality and efficiency do not highlight the specific advantages that separate sampling provides in practical scenarios.