What is the main purpose of stratified random sampling?

The main purpose of stratified random sampling is to ensure diversity in the data collected. This sampling method involves dividing the entire population into distinct subgroups, known as strata, that share similar characteristics. These strata can be based on specific attributes such as age, income, education level, or geographic location. By sampling from each of these strata proportionally, researchers can obtain a more representative sample of the overall population.

This approach enhances the diversity of the data because it guarantees that various segments are adequately represented in the sample. Consequently, the results derived from stratified random sampling are more accurate and reliable, particularly when analyzing how different groups within the population may respond to the research questions. It captures the variability of the population and helps prevent potential issues that might arise from a sample that lacks representation of certain segments.

The other options provide insights but don't align with the primary purpose of stratified random sampling. While targeting a specific demographic can occur when defining strata, the emphasis is more on representation rather than targeting. Simplifying data processing may be a byproduct of better-structured data, but it is not a primary goal. Finally, while stratified random sampling aims to reduce bias, it does not eliminate it completely, as other sources of bias can still exist.