Which sampling approach is likely to yield results that better reflect the overall population?

Stratified sampling is an effective approach for obtaining results that better reflect the overall population because it involves dividing the population into distinct subgroups, or strata, that share similar characteristics. This method ensures that each subgroup is adequately represented in the sample, which enhances the generalizability of the findings.

By using stratified sampling, researchers can control for the variations within different segments of the population, allowing for more accurate comparisons and insights. For example, if a population is composed of different age groups, socioeconomic statuses, or ethnic backgrounds, stratified sampling guarantees that each of these groups is included in the research based on their proportion in the total population. This contrasts with methods such as convenience sampling, which may lead to biased results due to its reliance on readily accessible individuals, often overlooking other important sections of the population.

Simple random sampling and systematic sampling can also yield unbiased results if implemented correctly, but they do not specifically ensure representation of subgroups within the total population as effectively as stratified sampling does. Thus, stratified sampling stands out as the most reliable approach for achieving a sample that accurately reflects the overall population.