Understanding the Difference between Random Sampling and Stratified Sampling

Random sampling and stratified sampling are two commonly used methods in the field of statistics for selecting individuals from a population to form a sample. While both techniques serve the purpose of obtaining a representative sample, they differ in their approach and suitability for different scenarios. In this article, we will explore the key differences between random sampling and stratified sampling, their applications, and the advantages they offer.

Random Sampling

Random sampling is a method of selecting individuals from a population in a way that each individual has an equal chance of being chosen. It is used to obtain a representative sample of the entire population. This sampling technique is suitable when the population is homogeneous or uniform, and there is no significant variation among the elements. Random sampling is often employed when there is limited information available about the population or when the population has too many differences to divide into subsets.

One of the key advantages of random sampling is its simplicity. It is less time-consuming and less expensive compared to other sampling methods. By selecting individuals randomly, it minimizes the potential for bias and ensures that every member of the population has an equal opportunity to be included in the sample.

Stratified Sampling

Stratified sampling involves dividing the population into smaller groups called strata based on shared characteristics. This sampling technique is used when researchers want to study subgroups or variables within the population. By dividing the population into strata, stratified sampling ensures that each subgroup is adequately represented in the sample. This approach is recommended when the population is heterogeneous or diverse, with distinct subgroups that exhibit different characteristics.

Stratified sampling offers greater precision in estimation compared to simple random sampling. It allows researchers to draw conclusions about specific subgroups within the population, as the sample is intentionally designed to include representatives from each stratum. This technique can be further classified into proportionate and disproportionate sampling. In proportionate stratified sampling, the sample size of each stratum is proportional to the population size of the stratum. In contrast, disproportionate stratified sampling allows for variations in the sizes of the strata, which may not be proportional to their occurrence in the population.

In summary, random sampling and stratified sampling are two distinct approaches to selecting individuals from a population for research purposes. Random sampling is suitable when the population is homogeneous and there is limited information available. On the other hand, stratified sampling is used to study subgroups or variables within a diverse population, offering greater precision in estimation. While random sampling is simpler and less expensive, stratified sampling provides a more targeted and representative sample.

Sources:

Investopedia. “What Is the Difference Between a Simple Random Sample and a Stratified Random Sample?” (https://www.investopedia.com/ask/answers/042415/what-difference-between-simple-random-sample-and-stratified-random-sample.asp)
LinkedIn. “How Do You Choose Between Simple Random and Stratified Sampling?” (https://www.linkedin.com/advice/3/how-do-you-choose-between-simple-random-stratified-sampling)
Investopedia. “Stratified Random Sampling” (https://www.investopedia.com/terms/stratified_random_sampling.asp)

FAQs

What is random sampling?

When is random sampling suitable?

Random sampling is suitable when the population is homogeneous or uniform, and there is no significant variation among the elements. It is often used when there is limited information available about the population or when the population has too many differences to divide into subsets.

What is stratified sampling?

Stratified sampling involves dividing the population into smaller groups called strata based on shared characteristics. It is used when researchers want to study subgroups or variables within the population.

What is the advantage of stratified sampling?

Stratified sampling ensures that each subgroup is adequately represented in the sample, providing more precise estimations for specific subgroups within the population.

When should stratified sampling be used?

Stratified sampling is recommended when the population is heterogeneous or diverse, with distinct subgroups that exhibit different characteristics.

What is the difference between random sampling and stratified sampling?

The key difference lies in their approach and suitability for different scenarios. Random sampling is simpler and less expensive, suitable for homogeneous populations with limited information. Stratified sampling is more targeted, allowing researchers to study subgroups within a diverse population, providing greater precision in estimation.

What is the difference between proportionate and disproportionate stratified sampling?

In proportionate stratified sampling, the sample size of each stratum is proportional to the population size of the stratum. In disproportionate stratified sampling, the sizes of the strata are not proportional to their occurrence in the population.

Which sampling method is best?

The choice between random sampling and stratified sampling depends on the research objectives and characteristics of the population. Random sampling is simpler and less resource-intensive, while stratified sampling offers targeted insights into specific subgroups within a diverse population.