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In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender identity, location, etc.). A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. Stratified sampling is a method of obtaining a representative sample from a population that researchers have divided into relatively similar subpopulations (strata)
Researchers use stratified sampling to ensure specific subgroups are present in their sample. Accordingly, application of stratified sampling method involves dividing population into different subgroups (strata) and selecting subjects from each strata in a proportionate manner. In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations
In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently.
Stratified sampling is a method of sampling that involves dividing a population into homogeneous subgroups or ‘strata’, and then randomly selecting individuals from each group for study The process of classifying the population into groups before sampling is called stratification. Stratified random sampling helps you pick a sample that reflects the groups in your participant population Our ultimate guide gives you a clear definition, example, and process for doing it yourself.
Stratified sampling is a sampling technique used in statistics and machine learning to ensure that the distribution of samples across different classes or categories remains representative of the population. Estimate population proportions when stratified sampling is used
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