Editing Stratified sampling (section)

==Strategies==
#''Proportionate allocation'' uses a [[sampling fraction]] in each of the strata that are proportional to that of the total population. For instance, if the population consists of ''n'' total individuals, ''m'' of which are male and ''f'' female (and where ''m'' + ''f'' = ''n''), then the relative size of the two samples (''x''<sub>1</sub> = ''m''/''n'' males, ''x''<sub>2</sub> = ''f''/''n'' females) should reflect this proportion.
#''Optimum allocation'' (or ''disproportionate allocation'') – The sampling fraction of each stratum is proportionate to both the proportion (as above) and the [[standard deviation]] of the distribution of the variable. Larger samples are taken in the strata with the greatest variability to generate the least possible overall sampling variance. [[Neyman allocation]] is a strategy of this type.

A real-world example of using stratified sampling would be for a political [[Statistical survey|survey]]. If the respondents needed to reflect the diversity of the population, the researcher would specifically seek to include participants of various minority groups such as race or religion, based on their proportionality to the total population as mentioned above. A stratified survey could thus claim to be more representative of the population than a survey of [[simple random sampling]] or [[systematic sampling]]. Both mean and variance can be corrected for disproportionate sampling costs using [[Sample_size_determination#Stratified_sample_size|stratified sample sizes]].