Editing Stratified sampling (section)

{{Short description|Sampling from a population which can be partitioned into subpopulations}}
{{more citations needed|date=December 2020}}
In [[statistics]], '''stratified sampling''' is a method of [[sampling (statistics)|sampling]] from a [[Population (statistics)|population]] which can be [[partition of a set|partitioned]] into [[subpopulation]]s.

[[File:Stratified_sampling.PNG|thumb|Stratified sampling example]]
In [[statistical survey]]s, when subpopulations within an overall population vary, it could be 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 define a partition of the population. That is, it should be ''[[Collectively exhaustive events|collectively exhaustive]]'' and ''[[Mutual exclusivity|mutually exclusive]]'': every element in the population must be assigned to one and only one stratum. Then sampling is done in each stratum, for example: by [[simple random sampling]]. 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.

In [[computational statistics]], stratified sampling is a method of [[variance reduction]] when [[Monte Carlo method]]s are used to estimate population statistics from a known population.<ref name="varred17">{{cite journal|last1=Botev|first1=Z.|last2=Ridder|first2=A.|title=Variance Reduction|journal= Wiley StatsRef: Statistics Reference Online|date=2017|pages=1–6|doi=10.1002/9781118445112.stat07975|isbn=9781118445112}}</ref>