What does a stratified sample ensure compared to a random sample?

A stratified sample is designed to ensure that different subgroups or strata within a population are adequately represented in the sample. This methodology breaks down the population into distinct categories, such as age, income level, education, or any other relevant characteristic. By doing so, a stratified sample provides a more comprehensive and representative picture of the entire population compared to a random sample, which may inadvertently overlook certain segments or categories.

When a sample is stratified, researchers can ensure that each subgroup is proportionally represented, which enhances the overall validity and generalizability of the results. This method reduces the risk of sampling bias that might occur in a purely random sampling approach, where some groups might be underrepresented or overrepresented by chance.

In contrast, a random sample might not capture important variations within a population, potentially leading to misleading conclusions if certain characteristics are not included. Thus, stratified sampling is particularly valuable in research endeavors where understanding the differences between groups is essential for accurate analysis and insights.