Which sampling method ensures that the proportions of subgroups in the sample match the proportions in the population?

Which sampling method ensures that the proportions of subgroups in the sample match the proportions in the population?

• A. Convenience sampling
• B. Cluster sampling
• C. Stratified sampling
• D. Simple random sampling

Answer: (C)

Stratified sampling is the method that ensures the proportions of subgroups in the sample reflect the proportions in the overall population. In this approach, the population is divided into distinct subgroups, known as strata, based on specific characteristics such as age, gender, income level, or other relevant factors. This division allows researchers to ensure that each subgroup is adequately represented in the sample, maintaining the same proportional representation as found in the broader population. For instance, if a population consists of 60% females and 40% males, stratified sampling enables the sample to mirror these proportions by selecting a corresponding number of females and males. This technique enhances the precision of the results because it focuses on the variances within each subgroup, leading to more reliable and valid conclusions. The other sampling methods, such as convenience sampling, cluster sampling, and simple random sampling, do not guarantee that the sample will maintain the same proportions of subgroups as present in the population. For instance, convenience sampling may lead to a sample that is biased because it only includes members of the population that are easiest to reach, without regard to their actual proportion in the larger group. Similarly, while cluster sampling involves dividing the population into clusters, it does not specifically account for the proportional representation

Stratified sampling is the method that ensures the proportions of subgroups in the sample reflect the proportions in the overall population. In this approach, the population is divided into distinct subgroups, known as strata, based on specific characteristics such as age, gender, income level, or other relevant factors. This division allows researchers to ensure that each subgroup is adequately represented in the sample, maintaining the same proportional representation as found in the broader population.

For instance, if a population consists of 60% females and 40% males, stratified sampling enables the sample to mirror these proportions by selecting a corresponding number of females and males. This technique enhances the precision of the results because it focuses on the variances within each subgroup, leading to more reliable and valid conclusions.

The other sampling methods, such as convenience sampling, cluster sampling, and simple random sampling, do not guarantee that the sample will maintain the same proportions of subgroups as present in the population. For instance, convenience sampling may lead to a sample that is biased because it only includes members of the population that are easiest to reach, without regard to their actual proportion in the larger group. Similarly, while cluster sampling involves dividing the population into clusters, it does not specifically account for the proportional representation