Stratified random sampling approaches first divides the population into subgroups based on one or more variables of interest.
A method of sampling from a population that may be divided into subpopulations is known as stratified sampling in statistics.
Before sampling, a population is stratified by separating its members into uniform subgroups. The population should be divided up according to the strata. This means that each component of the population must be assigned to a single stratum in order for it to be mutually exclusive and collectively exhaustive. Then each stratum is subjected to basic random sampling. Reducing sampling error is the goal in order to increase sample accuracy. In comparison to the arithmetic mean of a straightforward random sampling of the population, it can generate a weighted mean with less fluctuation.
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