Stratified Sample
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definiton
The population is divided into subgroups (strata) based on shared characteristics (e.g., age, gender). A random sample is then drawn from each stratum.- Example: Separating a school population into grades and sampling each grade.
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Key Features:
- Strata are homogeneous within but heterogeneous between.
- Can be proportionate (sample size per stratum matches population proportion) or disproportionate (oversampling small strata).
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Advantages:
- Ensures representation of all subgroups.
- Increases precision (lower sampling error).
- Allows targeted analysis of strata.
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Disadvantages:
- Requires prior knowledge of population structure.
- More complex to design and execute.
Key Differences Vs. SRS
| Feature | SRS | Stratified Sampling |
|---|---|---|
| Subgroups | Ignores subgroups | Explicitly defines strata |
| Precision | Lower for heterogeneous populations | Higher due to reduced variability |
| Implementation | Simpler | More complex |
| Use Case | Homogeneous populations | Populations with distinct subgroups |
When to Use
- SRS: Best for small, homogeneous populations or when no prior subgroup information exists.
- Stratified: Ideal for populations with distinct subgroups needing precise representation (e.g., clinical trials, demographic surveys).