Editing Stratified sampling (section)

==Disadvantages==
It would be a misapplication of the technique to make subgroups' sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup sizes (or to their variances, if known to vary significantly—e.g. using an [[F-test|F test]]). Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling. If subgroup variances differ significantly and the data needs to be stratified by variance, it is not possible to simultaneously make each subgroup sample size proportional to subgroup size within the total population. For an efficient way to partition sampling resources among groups that vary in their means, variance and costs, see [[Sample size#Stratified sample size|"optimum allocation"]].
The problem of stratified sampling in the case of unknown class priors (ratio of subpopulations in the entire population) can have a deleterious effect on the performance of any analysis on the dataset, e.g. classification.<ref name=minimax-sampling/> In that regard, [[minimax|minimax sampling ratio]] can be used to make the dataset robust with respect to uncertainty in the underlying data generating process.<ref name=minimax-sampling/>

Combining sub-strata to ensure adequate numbers can lead to [[Simpson's paradox]], where trends that exist in different groups of data disappear or even reverse when the groups are combined.