What is random stratified sampling method?

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Random Stratified Sampling Method

In stratified random  sampling, the strata are formed based on members' shared attributes or characteristics. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample.

The reasons to use stratified sampling rather than simple random sampling include

If measurements within strata have lower standard deviation, stratification gives smaller error in estimation.

For many applications, measurements become more manageable and/or cheaper when the population is grouped into strata.

It is often desirable to have estimates of population parameters for groups within the population.

If the population density varies greatly within a region, stratified sampling will ensure that estimates can be made with equal accuracy in different parts of the region, and that comparisons of sub-regions can be made with equal statistical power. For example, in Ontario a survey taken throughout the province might use a larger sampling fraction in the less populated north, since the disparity in population between north and south is so great that a sampling fraction based on the provincial sample as a whole might result in the collection of only a handful of data from the north.

Randomized stratification can also be used to improve population representativeness in a study.

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