What effect does stratified sampling have on study results?

Stratified sampling is a method used in research to ensure that different subgroups within a population are represented in the sample. By dividing the population into distinct strata, or layers, based on specific characteristics, researchers can effectively capture the diversity present in the entire population. This approach helps in obtaining a more representative sample, which is crucial for enhancing the validity of the study's results. For instance, if studying a particular ecosystem, stratified sampling could ensure that various habitats, like wetlands, forests, and grasslands, are included proportionally based on their prevalence in the overall area being studied.

This method can significantly improve the reliability of the conclusions drawn from the data, as it accounts for factors that might affect the outcome. In contrast, methods that do not incorporate stratification may lead to overrepresentation or underrepresentation of certain subgroups, which can skew the results and reduce their applicability to the broader population. Stratified sampling is therefore invaluable for studies requiring accurate reflections of different segments within a diverse population.