In stratified sampling, how are the population groups divided?

Stratified sampling is a technique where the population is first divided into distinct subgroups or strata, which are based on specific characteristics that are relevant to the research question (e.g., age, income, or gender). Once these strata are identified, a sample is taken from each group, often in proportion to the size of the stratum in the overall population. Stratified sampling ensures that key subgroups are adequately represented in the sample, making it particularly useful when certain segments of the population may be underrepresented in a simple random sample. This technique increases the precision of the estimates by reducing sampling variability within each group. The other options are incorrect because: • Option 1 (Random selection) describes random sampling, not stratified sampling. • Option 3 (Systematic selection) is related to systematic sampling, not stratified sampling. • Option 4 (Data collection) is not specific to stratified sampling, where the strata are defined before sampling. • Option 5 (Variance) is a characteristic of the population, but it is not the basis for dividing the population in stratified sampling.