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In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. Stratified sampling example In statistical surveys , when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation ( stratum ) independently.
Graphic breakdown of stratified random sampling. In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the ...
There are several potential benefits to stratified sampling. [12] First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.
Stratified purposive sampling is a type of typical case sampling, and is used to get a sample of cases that are "average", "above average", and "below average" on a particular variable; this approach generates three strata, or levels, each of which is relatively homogeneous, or alike. [1]
There are many reasons to use stratified sampling: [7] to decrease variances of sample estimates, to use partly non-random methods, or to study strata individually. A useful, partly non-random method would be to sample individuals where easily accessible, but, where not, sample clusters to save travel costs.
Different sampling designs and statistical adjustments may have substantially different impact on the bias and variance of estimators (such as the mean). [citation needed] An example of a design which can lead to estimation efficiency, compared to simple random sampling, is Stratified sampling. This efficiency is gained by leveraging ...
The common random numbers variance reduction technique is a popular and useful variance reduction technique which applies when we are comparing two or more alternative configurations (of a system) instead of investigating a single configuration. CRN has also been called correlated sampling, matched streams or matched pairs.
Minimisation is a method of adaptive stratified sampling that is used in clinical trials, as described by Pocock and Simon. [1] [2]The aim of minimisation is to minimise the imbalance between the number of patients in each treatment group over a number of factors.