What is stratified sampling?

Stratified sampling is a method that involves dividing a population into distinct subgroups, known as strata, based on shared characteristics. This technique ensures that each subgroup is appropriately represented in the sample, which enhances the accuracy and reliability of the findings. By stratifying the population, researchers can perform analyses that take these different groups into account, leading to more nuanced insights that might be missed if the sample were taken indiscriminately from the overall population.

This method is particularly useful in healthcare research where different demographics, conditions, or other defining factors may influence study outcomes. For instance, if a study focuses on patient outcomes from various socioeconomic backgrounds, stratified sampling would allow researchers to ensure that all relevant groups are included, thereby enhancing the applicability of the results across the entire population.

In contrast, other methods, such as focusing solely on one group of interest, may overlook important variations, while random sampling from the entire population could potentially underrepresent smaller, but significant subgroups. Similarly, choosing samples that are easily accessible might lead to a biased representation that does not reflect the broader population accurately. Stratified sampling effectively mitigates these issues by purposefully incorporating distinct segments of the population, improving the overall validity of the research findings.