What method is used in stratified random sampling?

Stratified random sampling involves dividing the population into distinct subgroups or strata that share similar characteristics, and then taking samples from each of these strata. The purpose of this approach is to ensure that the sampling is representative of the entire population by including various segments.

By dividing the lot into equal sections and sampling each one, the method ensures that different characteristics of the lot are captured in the sample, which could reflect variability in soil types, aggregate quality, or other relevant factors. This can lead to more accurate and reliable results as each subgroup is adequately represented.

Other methods listed do not provide the same level of representation. For instance, sampling the entire lot at once could lead to a biased sample if certain areas of the lot are not adequately represented. Randomly sampling from a single sublot limits the diversity of the sample and may not capture the variations throughout the entire lot. Combining samples from different lots does not maintain the integrity of the sampling process and can obscure the specific characteristics of individual lots.