In stratified sampling, the population is partitioned into non-overlapping groups, called strata and a sample is selected by some design within each stratum.
For example, geographical regions can be stratified into similar regions by means of some known variable such as habitat type, elevation or soil type. Another example might be to determine the proportions of defective products being assembled in a factory. In this case sampling may be stratified by production lines, factory, etc.
Can you think of a couple additional examples where stratified sampling would make sense? Look for opportunities when the measurements within the strata are more homogeneous.
The principal reasons for using stratified random sampling rather than simple random sampling include:
1. Stratification may produce a smaller error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are very homogeneous.
2. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.
3. Estimates of population parameters may be desired for subgroups of the population. These subgroups should then be identified.