What is stratified sampling?

What is stratified sampling?

• A. Sampling that ignores population subgroups
• B. Random sampling from a single homogeneous group
• C. Dividing a population and sampling from each subgroup
• D. Sampling that selects every nth individual

Answer: (C)

Stratified sampling is a method that enhances the representativeness of a sample by carefully considering the diversity within a population. This technique involves dividing the entire population into distinct subgroups, known as strata, which share similar characteristics. These strata could be based on various factors such as age, gender, income level, or any other relevant criteria that are pertinent to the research. Once the population is divided into these subgroups, researchers then perform random sampling from each stratum. This ensures that all subgroups are fairly represented in the final sample, allowing for more generalizable and reliable results. This approach is particularly beneficial as it helps address the variability within the population and provides a more comprehensive understanding of the various segments within it. In contrast, sampling that ignores population subgroups does not account for the diversity within the population and may lead to biased results. Random sampling from a single homogeneous group focuses on a specific segment only, which fails to capture the overall diversity. Selecting every nth individual, while systematic, does not guarantee representation of all subgroups and may lead to similar biases. Stratified sampling, therefore, is a more sophisticated approach to ensuring that the sample reflects the whole population accurately.

Stratified sampling is a method that enhances the representativeness of a sample by carefully considering the diversity within a population. This technique involves dividing the entire population into distinct subgroups, known as strata, which share similar characteristics. These strata could be based on various factors such as age, gender, income level, or any other relevant criteria that are pertinent to the research.

Once the population is divided into these subgroups, researchers then perform random sampling from each stratum. This ensures that all subgroups are fairly represented in the final sample, allowing for more generalizable and reliable results. This approach is particularly beneficial as it helps address the variability within the population and provides a more comprehensive understanding of the various segments within it.

In contrast, sampling that ignores population subgroups does not account for the diversity within the population and may lead to biased results. Random sampling from a single homogeneous group focuses on a specific segment only, which fails to capture the overall diversity. Selecting every nth individual, while systematic, does not guarantee representation of all subgroups and may lead to similar biases. Stratified sampling, therefore, is a more sophisticated approach to ensuring that the sample reflects the whole population accurately.