Stratified Sampling: Definition, Methods & Examples

7 min read

Updated 2026-02-01

Guide

Stratified sampling is a probability sampling technique that divides a population into distinct subgroups (strata) before randomly selecting samples from each stratum. This method ensures representation of key demographic or characteristic groups.

Key Takeaways

•Stratified sampling divides populations into homogeneous subgroups before sampling
•It guarantees representation of important subgroups that might be missed in simple random sampling
•Proportionate stratification maintains population ratios; disproportionate over-samples small groups
•Reduces sampling error compared to simple random sampling
•Requires prior knowledge of population characteristics to define strata

What Is Stratified Sampling?

Stratified sampling is a method where the researcher divides the entire population into distinct subgroups called strata, then randomly selects samples from each stratum. Members within each stratum share a common characteristic.

Types of Stratified Sampling

Proportionate: Sample sizes from each stratum are proportional to the stratum's size in the population.

Disproportionate: Sample sizes are intentionally different from population proportions to ensure adequate representation of small subgroups.

When to Use Stratified Sampling

Use when: subgroup analysis is important, population has known distinct groups, small subgroups must be represented, and precision is critical.

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Frequently Asked Questions

In stratified sampling, you sample from EVERY stratum. In cluster sampling, you select SOME clusters and sample everyone within chosen clusters.

For proportionate sampling, multiply your total sample size by each stratum's proportion. For disproportionate, allocate based on desired precision.

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