What does stratified sampling involve?

Stratified sampling involves dividing a population into subgroups, known as strata, based on shared characteristics such as age, gender, income level, or education. This method is particularly effective in ensuring that different segments of a population are adequately represented in the sample. By identifying these subgroups, researchers can obtain more precise and reliable results, as it minimizes sampling bias and provides a more comprehensive overview of the population being studied.

For instance, if a researcher wants to understand the health trends among different age groups in a community, they might stratify subjects into categories such as children, adults, and seniors. This way, the sample reflects the diversity of the entire population, allowing for comparisons between these different age strata.

The other options do not accurately describe stratified sampling. Dividing subjects into equal groups does not necessarily consider the characteristics of the population; simply selecting random points may overlook important demographic variations; and utilizing only the most convenient subjects would lead to biased results, as it does not provide a representative sample of the whole population.