Stratified sampling is a type of sampling design that randomly collects samples from distinct subgroups based on a shared characteristic. May 15, 2025 · Stratified sampling is a sophisticated method that helps achieve greater representativeness by dividing the population into distinct subgroups—or strata —and then sampling from each group appropriately. . Sep 18, 2020 · In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e. ). In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Jul 31, 2023 · 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. May 8, 2025 · 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. Jul 23, 2025 · Stratified Random Sampling is a technique used in Machine Learning and Data Science to select random samples from a large population for training and test datasets. These samples represent a population in a study or a survey.

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