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The problem of stratified sampling in the case of unknown class priors (ratio of subpopulations in the entire population) can have 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/>
In general the size of the sample in each stratum is taken in proportion to the size of the stratum. This is called proportional allocation. Suppose that in a company there are the following staff:<ref>{{cite web |last=Hunt |first=Neville |last2=Tyrrell |first2=Sidney |year=2001 |url=http://nestor.coventry.ac.uk/~nhunt/meths/strati.html |title=Stratified Sampling |work=Webpage at Coventry University |accessdate=12 July 2012 }}</ref>
*male, full-time: 90
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