<Tr> <Td> </Td> <Td> This article needs additional citations for verification . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed . (July 2012) (Learn how and when to remove this template message) </Td> </Tr> <P> In statistics, stratified sampling is a method of sampling from a population . </P> <P> In statistical surveys, when subpopulations within an overall population vary, it is advantageous to sample each subpopulation (stratum) independently . Stratification is the process of dividing members of the population into homogeneous subgroups before sampling . The strata should be mutually exclusive: every element in the population must be assigned to only one stratum . The strata should also be collectively exhaustive: no population element can be excluded . Then simple random sampling or systematic sampling is applied within each stratum . The objective is to improve the precision of the sample by reducing sampling error . It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population . </P> <P> In computational statistics, stratified sampling is a method of variance reduction when Monte Carlo methods are used to estimate population statistics from a known population . </P>

When should a researcher use stratified random sampling