An Explanation of Stratified Random Sampling

Stratified random sampling is a type of random sampling that includes surveying a selected group of subjects from a larger group of people. Through this useful sampling method, researchers can sample each subpopulation of the main population separately. This is an effective and easy way to get unbiased results from these populations.

What is the Stratified form of Random Sampling?

When random sampling is stratified, researchers must divide the population being surveyed into exclusive subgroups. Every member of the subgroup must have the dividing element in common; in many cases, populations are divided by sex, race, or income. Random sampling is then conducted in each group, or stratum. In order to get the most accurate results, researchers must take care in ensuring that no population element has been excluded during stratification.


There are two main strategies when performing this popular form of sampling. The first is through proportionate allocation, in which a sampling fraction in each group is used that is proportionate to the total population. The second is optimum allocation, during which each group is proportionate to the standard deviation off the variable’s distribution. These strategies help ensure that the results have the least possible sampling variance and are as accurate as possible.

Advantages of this Method

The biggest advantage of sampling that has been stratified is that it produces results that are both largely unbiased and accurate. When surveys are conducted by via stratified sampling, they often produce data that is more representative of the entire population because of the special attention it pays to the smaller subgroups within the population. It is also the best way to obtain results that reflect the diversity of the population in question. This advantage makes stratified sampling much more effective than simple sampling for large and diverse populations.

Disadvantages of Stratified Sampling

Disadvantages of this method are the same as the disadvantages of any survey – there is always room for a sampling error. Also, it may not be right for every survey situation. For example, it wouldn’t make sense to use this approach when surveying a population whose subjects are not similar enough to create subgroups or when there is not as much data available from one subgroup as there is from the others.

When to Use It

It is a good idea to use stratified sampling when surveying a large population that is very diverse – and especially when the results need to reflect that diversity. This popular sampling method is often used in political surveys when trying to determine opinions and predict poll outcomes between people of different minorities, religions, or sexes. It can also be useful when population density varies greatly among different areas of the same country.

Stratified Random Sampling, when used among a diverse population with many different subpopulations, can be the best way to get accurate and unbiased results from your survey. Through this method of dividing up the population in question before applying traditional random sampling techniques, it’s possible to ensure the least possible amount of sampling error while receiving data efficiently to draw conclusions about the population being studied.

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