a sample chosen by stratified random sampling involves

A Sample Chosen by Stratified Random Sampling Involves

When it comes to collecting data from a population, researchers often rely on various methods of sampling. However, one method stands out for its ability to provide an accurate representation of diverse populations: stratified random sampling. Unlike other methods, such as simple random sampling, stratified sampling ensures that each subgroup in the population is represented equally and accurately.

In a population with multiple subgroups, it's not uncommon for some subgroups to be less common than others. This can lead to a low sample size for those subgroups, which can result in inaccurate data. Stratified sampling eliminates this problem by dividing the population into mutually exclusive and exhaustive subgroups, ensuring that every member of the population is included.

The process of stratified random sampling involves several steps. First, researchers must define their population and identify the characteristics they want to study. Next, they separate the population into strata based on those characteristics. For example, if a researcher is studying the effects of climate change on different regions, they might divide the population into strata based on geographical location.

Once the strata are defined, researchers must decide on the sample size for each stratum. This ensures that every subgroup is represented equally in the sample. Finally, the researcher randomly samples from each stratum using another probability sampling method, such as cluster sampling or simple random sampling.

Stratified sampling has several advantages over other methods of sampling. For one, it ensures that every member of the population is included and accurately represented. This helps to eliminate research biases like undercoverage bias and increases the validity of the study. Additionally, stratified sampling allows researchers to estimate statistical measures for each sub-population, providing a more accurate representation of the data.

In summary, stratified random sampling involves dividing a population into homogeneous subpopulations based on specific characteristics, then randomly sampling from each stratum using another probability sampling method. This method provides an accurate representation of diverse populations and is essential for researchers who want to ensure that every member of the population is included and accurately represented in their study.

A sample chosen by stratified random sampling involves