AI engineering glossary · Probability
What is stratified sampling?
Stratified sampling: A sampling design that first divides the population into non-overlapping groups called strata, then draws a random sample from each. It guarantees representation of every group and can reduce variability.
Stratified sampling: an example
A survey of 1,000 shoppers picks 400 from Tier-1 cities, 400 from Tier-2 and 200 from Tier-3, matching their share of the market.
Related terms
More Probability terms
- Survivorship bias
- Systematic sampling
- Transition matrix
- Uniform distribution
- Union of events
- Variance
- Z-score
- Addition rule
Where you learn it
Stratified sampling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.