Stratified Sampling: What it is and How it Works

Understand the meaning, definition, and explanation of stratified sampling: learn how it works

What is Stratified Sampling?

Stratified sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In statistical terms, these strata represent subsets of the relevant population in which each subset is associated with a particular criterion or characteristic. The main objective of stratified sampling is to ensure that each stratum is adequately represented within the whole sample population of a research study.

Why is Stratified Sampling Used?

Stratified sampling is used when a population is not homogeneous or uniformly distributed. It is a valuable type of sampling method in research because it allows the researchers to examine the relationships within and between specific strata, so they can draw more accurate conclusions about the population as a whole. Stratified sampling is particularly useful when dealing with large populations.

How Does Stratified Sampling Work?

The process of stratified sampling begins by identifying the strata or subgroups within the population. These strata are based on specific characteristics that are relevant to the research question. For example, a researcher studying educational achievement might divide a population into strata based on age, socioeconomic status, or ethnicity.

Once the strata are identified, the researcher randomly selects individuals from each stratum to participate in the study. The number of individuals selected from each stratum can either be proportional to the size of the stratum in relation to the population (proportional stratified sampling) or the same number can be selected from each stratum regardless of its size (non-proportional stratified sampling).

Proportional Stratified Sampling

In proportional stratified sampling, the size of the sample from each stratum is proportionate to the population size of the stratum. This means that a larger number of individuals will be selected from larger strata, and a smaller number from smaller strata. This method ensures that the sample accurately reflects the population composition in terms of the strata.

Non-Proportional Stratified Sampling

In non-proportional stratified sampling, the same number of individuals is selected from each stratum, regardless of the stratum’s size in relation to the population. This method is used when the researcher wants to ensure that smaller strata are adequately represented in the sample.

Advantages and Disadvantages of Stratified Sampling


  • Stratified sampling can provide a higher statistical precision than simple random sampling, especially when the population is divided into disparate subgroups.
  • It ensures that each subgroup within the population receives proper representation within the sample.
  • It allows for the examination of relationships within and between strata.


  • Stratified sampling can be more complex and time-consuming than simple random sampling.
  • It requires a thorough understanding of the population and the characteristics being used to create the strata.
  • It may not be possible to divide every population into clear and distinct strata.


Stratified sampling is a valuable tool in statistical research and data analysis. It allows for greater precision and ensures that all subgroups within a population are adequately represented. However, it requires careful planning and a deep understanding of the population being studied. Despite its challenges, when used correctly, stratified sampling can provide valuable insights and contribute to the validity and reliability of research findings.


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