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Stratified random sampling is a technique best used with a sample population easily broken into distinct subgroups.  Samples are then taken from each subgroup based on the ratio of the subgroup’s size to the total data population.

For example, assume a total data population of 1000, broken into four subgroups with data populations as follows:

A: 450

B: 250

C: 200

D: 100

To perform stratified random sampling with 200 pieces of the data, 45% of the sample must come from A, 25% must come from B, 20% must come from C and 10% must come from D.   This yields a sample of 90 samples from A, 50 samples from B, 40 samples from C and 20 samples from D, for a total of 200.

Using stratified random samples insures that there will be selections from each subgroup.  Otherwise, there is a chance that one subgroup might be omitted, and that subgroup’s characteristics would not be included in the statistical analysis of the sample. 

Stratified random sampling is used in the investment world to create a portfolio that replicates an index, such as a bond index.  Rather than incur the high costs of purchasing all the thousands of bonds in the specific bond index, a portfolio manager creates a sample replication of the index.  He does this by using stratified random sampling to make sure bonds of all types within the index are included in the sample.

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