When a lot is divided into sublots and samples are determined randomly within each sublot, this type of sampling is termed:

The correct answer reflects the practice of stratified random sampling, which is utilized when a lot is divided into distinct sublots. In this methodology, the objective is to ensure that each sublot is represented in the sample, which accounts for the potential variability within each sublot. By selecting samples randomly from within each sublot, this technique helps to provide a more accurate and representative assessment of the entire lot's quality and characteristics.

Stratified random sampling enhances the reliability of the results by minimizing the risk of bias that may occur if the entire lot were treated as a single entity. It acknowledges that even within a single lot, different segments (sublots) might exhibit variations due to factors such as material consistency, production processes, or environmental influences.

Using this approach ultimately yields a clearer picture of the overall quality, as the samples drawn from the individual sublots collectively represent the variability within the larger lot. This technique is particularly advantageous in quality control scenarios where measurement accuracy and representation are critical.