{"id":438269,"date":"2022-08-20T09:00:18","date_gmt":"2022-08-20T16:00:18","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=438269"},"modified":"2026-07-30T00:58:09","modified_gmt":"2026-07-30T07:58:09","slug":"stratified-sampling","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/stratified-sampling\/","title":{"rendered":"Stratified Sampling: Definition, Types, Difference & Examples"},"content":{"rendered":"\n

Stratified sampling is a probability sampling method. It divides a population into distinct, non-overlapping subgroups, called strata, then draws a random sample from each one. Researchers use it to guarantee every important subgroup shows up in the final sample, instead of leaving representation to chance.<\/p>\n\n\n\n

You may also see this method called stratified random sampling. Both names describe the same technique. Random selection happens inside each stratum, not across the whole population at once. This works best when a population is too varied for a simple random sample to represent fairly. Splitting a heterogeneous group into homogeneous strata produces far more precise estimates.<\/p>\n\n\n\n

In this guide, we\u2019ll cover how stratified sampling works and its two main types. We\u2019ll also show when to use it over cluster or quota sampling, and how to size each stratum correctly.<\/p>\n\n\n\n\n\n

What is stratified sampling?<\/strong><\/h2>\n\n\n\n

Stratified sampling separates a population into mutually exclusive, homogeneous groups called strata. It then draws a random sample from each stratum instead of the population as a whole. It is one of the four core probability sampling<\/a> methods, alongside simple random, systematic, and cluster sampling. Every stratum should be internally similar, while strata should differ from each other on the trait that matters to the study.<\/p>\n\n\n\n

Consider a study on attitudes toward a policy issue across U.S. age groups. A researcher could survey the entire adult population, but a stratified sample of 2,000 people works just as well. Divide that sample into age strata, such as 18-29, 30-44, 45-59, and 60 and older. Each stratum then contributes its own random sample. The final group reflects the real population’s age spread, not whatever age range happened to respond first.<\/p>\n\n\n\n

The list a researcher draws these samples from is called the sampling frame. An accurate frame matters as much as the stratification itself. Missing segments (undercoverage) or duplicate entries (overcoverage) distort the sample, no matter how well the strata are defined.<\/p>\n\n\n\n

How does stratified sampling work?<\/strong><\/h2>\n\n\n\n

Stratified sampling follows a fixed sequence. Skipping a step, especially evaluating the sampling frame, is where most errors creep in. Here is the process from start to finish:<\/p>\n\n\n\n