Sampling
Definition
Sampling selects observations or possible outputs according to a defined procedure.
The selection of observations or the random drawing of outputs according to a defined sampling or probability procedure. Meaning and quality criteria depend on the statistical or generative context.
Simply explained
Instead of examining everything, appropriate samples are drawn, for example randomly selected parts of a shipment.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
For a quality inspection, 200 parts are randomly selected from a batch.
Limits and pitfalls
- Confusing statistical representativeness with random model decoding, presenting a convenience sample as representative, or concealing selection probabilities and dependencies.
Origin & history
Sampling is a fundamental concept of statistics, simulation, and probabilistic modeling.
Use in mid-sized companies
- Possible example: For a quality inspection, 200 parts are randomly selected from a batch.
Frequently asked questions
What does sampling mean?
The selection of observations or the random drawing of outputs according to a defined sampling or probability procedure. Meaning and quality criteria depend on the statistical or generative context. What mistakes should be avoided? Confusing statistical representativeness with random model decoding, presenting a convenience sample as representative, or concealing selection probabilities and dependencies.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: