In the context of sampling, what does "stratified random sampling" involve?

Stratified random sampling is a method used to ensure that different subgroups within a population are adequately represented in a sample. The correct answer highlights that this technique involves randomly selecting individuals from specific defined groups, referred to as strata. These strata are established based on shared characteristics, such as age, gender, income level, or any other relevant variable.

The purpose of stratified random sampling is to increase the precision and reliability of the sample by ensuring that important subgroups are not overlooked. For instance, if a population includes a significant number of individuals from various ethnic backgrounds, stratified sampling would ensure that each background is proportionately represented in the sample, leading to more accurate insights and conclusions about the entire population.

This method contrasts with other sampling techniques where the aim might not be to represent all subgroups or where selection might be entirely random without attention to specific characteristics. For example, simply choosing all members of a specific subgroup would not involve randomness, compromising the ability to generalize findings. Nevertheless, whenever stratified sampling is applied correctly, it leads to samples that are more representative of the entire population than non-stratified approaches.