Stratified Random Sampling

A sampling method in which a population group is divided into one or many distinct units – called strata – based on shared behaviors or characteristics

What is Stratified Random Sampling?

Stratified random sampling is a sampling method in which a population group is divided into one or many distinct units – called strata – based on shared behaviors or characteristics.


Stratified Random Sampling


Stratification refers to the process of classifying sampling units of the population into homogenous units. In stratified random sampling, any feature that explains differences in the characteristics of interest can be the basis of forming strata.

For example, people’s income or education level is a variation that can provide an appropriate backdrop for strata.



  • Stratified random sampling refers to a sampling technique in which a population is divided into discrete units called strata based on similar attributes, and the selection is done in a manner that is representative of the whole population.
  • The sampling technique is preferred in heterogeneous populations because it minimizes selection bias and ensures that the entire population group is represented.
  • It is not suitable for population groups with fewer attributes to be classified into relevant units.


Understanding Stratified Random Sampling

The assignment of the population is often an underlying challenge in statistical surveys. In this context, it means fixing the sampling field and estimating the universe parameter from a given data.

A stratified random sampling approach divides the population into relevant strata to increase a certain population group’s representativeness. However, it is only achievable if relevant strata are known and distinguishable in a population group.

In stratified random sampling, a researcher selects a small sample size with similar characteristics to represent a population group under study. A population being studied in a survey may be too large to be analyzed on an individual basis; hence, it is organized into groups with the same features to save costs and time.

The technique offers wide usage, such as estimating the income for varying populations, polling of elections, and life expectancy.


How Random Stratified Sampling Works

A researcher may select a more feasible approach to study a too-large population. An analysis is forced to divide the population into relevant strata prior to sampling.

One of the ways researchers use to select a small sample is called stratified random sampling. Estimates generated within strata are more precise than those from random sampling because dividing the population into homogenous groups often reduces sampling error and increases precision.

When seeking a potential stratum, it is always advisable to seek one that best minimizes variation in the characteristics under investigation and maximizes variation among strata. Stratified random sampling is best used with a heterogeneous population that can be divided using ancillary information.


Simple Random Sampling vs. Stratified Random Sampling


1. Assignment of the population

Simple random sampling – sometimes known as random selection – and stratified random sampling are both probability sampling procedures. The random selection in simple random sampling is meant to minimize bias. Thus, random selection ensures there is no bias and that the population group is represented entirely.

In contrast, the distinct subgroups called strata are based on similar attributes. The aspect of stratification, as a result, makes stratified random sampling a better choice for a population that varies widely.

Both simple and stratified random sampling entails sampling without replacement since they do not allow each case’s sample back into the sampling frame.


2. Robustness in sample selection

Overall, simple random sampling is more robust than stratified random sampling, especially when a population is characterized by too many differences to be categorized.

Simple random sampling is also effective in the case where a population group has one or scarce information that makes it hard to be subdivided into distinct units.

For instance, an online retail store may wish to survey its online customers’ purchasing habits to determine the future of its product line. If the store has approximately 50,000 customers, it may select 500 of these customers as the random sample. 500 is the sample frame within which customers are sampled purely at random.

To ensure the number of customers falls within the required range, the repeated selection is replaced. The retail store may then apply the estimated characteristics to the rest of the customers.

It can, therefore, be said that the chosen sample represents the entire customer population of 50,000. In this sense, a simple random sampling analyzes a more evenly dispersed sample throughout the population.


Strengths and Weaknesses of Stratified Random Sampling



Stratified random sampling captures the key attributes of a population group. As a result, it produces estimates representing the population because just like the weighted average, stratified random sampling provides a higher precision than simple random sampling.



Stratified random sampling is not, however, suitable in every survey. It works under the conditions that a population must be stratified using relevant attributes, failure to which it is rendered ineffective. It makes the stratified sampling technique useless when the population members cannot be classified into relevant units.


Additional Resources

CFI offers the Capital Markets & Securities Analyst (CMSA)™ certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following resources will be helpful:

  • Simple Random Sample
  • Sampling Errors
  • Sample Selection Bias
  • Statistical Significance

Financial Analyst Certification

Become a certified Financial Modeling and Valuation Analyst (FMVA)® by completing CFI’s online financial modeling classes and training program!