What is the difference between stratified sampling and random sampling?

Stratified sampling is a method in which the researcher divides the population into distinct subgroups, known as strata, based on specific characteristics or demographics relevant to the research. This division ensures that each subgroup is adequately represented in the sample. After this segmentation, random samples are taken from each stratum, and this process helps improve the precision and reliability of the results since it captures the diversity within the population.

The effectiveness of stratified sampling lies in its ability to yield more informative data by ensuring that important subgroups are not overlooked, which can occur in simple random sampling where every individual has an equal chance of selection regardless of their characteristics. This representation can enhance the quality of the analysis, making it particularly useful for studies focused on specific characteristics within a population.