How does stratified sampling increase efficiency?

Stratified sampling increases efficiency by sorting the population into categories, or strata, that share similar characteristics. This approach allows researchers to ensure that different segments of the population are represented proportionately in the sample. By dividing the population into these distinct groups based on relevant traits—such as age, income, or education level—researchers can then draw samples from each stratum.

This method enhances the precision of the results because it reduces variability within each stratum compared to the whole population. As a result, the conclusions drawn from the sample are more likely to reflect the true characteristics of the entire population, making the sampling method more efficient in achieving valid and reliable results. By ensuring adequate representation from each category, stratified sampling can yield more accurate insights while often requiring a smaller sample size than would be necessary in a simple random sampling approach.