Stratified Random Sampling


DEFINITION of 'Stratified Random Sampling'

A method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample.


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BREAKING DOWN 'Stratified Random Sampling'

The main advantage with stratified sampling is how it captures key population characteristics in the sample. Similar to a weighted average, this method of sampling produces characteristics in the sample that are proportional to the overall population. Stratified sampling works well for populations with a variety of attributes, but is otherwise ineffective, as subgroups cannot be formed.

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    Governments use various analytical techniques to examine the potential costs and benefits of policy options. One of those ... Read Full Answer >>
  2. How can I use systematic sampling with stratified sampling?

    Statistics offers different sampling methods for dealing with large populations; two common methods are systematic sampling ... Read Full Answer >>
  3. What are the best selection methods for creating a simple random sample?

    The best selection methods for selecting a simple random sample are the lottery method, using a random number table or having ... Read Full Answer >>
  4. What are the advantages and disadvantages of stratified random sampling?

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  5. What are some examples of stratified random sampling?

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