Stratified Sampling Task

AKA: Selective Sampling.
Context:
- It can be solved by a Stratified Sampling System (that implements a Stratified Sampling Algorithm).
Example(s):
- Stratified Dataset Item Selection.
- …
Counter-Example(s):
- Random Sampling.
See: Stratified Random Sampling, Population (Statistics), Statistical Survey, Simple Random Sampling, Systematic Sampling, Weighted Mean, Arithmetic Mean, Simple Random Sample, Variance Reduction, Monte Carlo Method, Data Item Selection Task.

References

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/stratified_sampling Retrieved:2014-8-2.
- In statistics, stratified sampling is a method of sampling from a population.
  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. This often improves the representativeness 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 methods are used to estimate population statistics from a known population.

Yoav Freund, Sebastian H. Seung, Eli Shamir and Naftali Tishby (1997). “Selective Sampling Using the Query by Committee Algorithm.” In: Machine Learning 28 (2-3).