Which sampling method involves dividing the population into groups and randomly selecting from these groups?

The correct answer is stratified sampling. This method involves first dividing the population into distinct, non-overlapping groups called strata based on certain characteristics such as age, income, or education level. Once the population is segmented into these groups, random samples are taken from each stratum. This ensures that each subgroup is represented in the final sample, which can provide more accurate and reliable estimates of the population parameters.

Stratified sampling is particularly useful when the researcher believes that the variable of interest may differ across strata, allowing for more nuanced insights compared to simple random sampling. By ensuring that subsets of the population are included, this method helps to minimize sampling bias and enhances the precision of the results, as it accounts for the variability within different groups.

In contrast, systematic sampling involves selecting samples based on a fixed interval from a randomly ordered list, cluster sampling focuses on dividing the population into clusters and then selecting entire clusters randomly, and random sampling simply refers to selecting individuals randomly from the entire population without considering the characteristics or divisions within it.