Stratified Random Sampling

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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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RELATED FAQS
  1. How does stratified random sampling influence government policy decisions?

    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?

    Researchers use stratified random sampling to obtain a sample population that best represents the entire population being ... Read Full Answer >>
  5. What are some examples of stratified random sampling?

    Simple random sampling is a sample of individuals that exist in a population; the individuals are randomly selected from ... Read Full Answer >>
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