Sampling involves selecting a subset of data from within a larger population of interest to draw statistical inferences about characteristics of the whole.
Key Features:
- Random assignment prohibits selection bias through a chance process
- Sample size influences analysis precision and generalizability
- Stratified sampling balances subgroup representation
Example:
A market research firm conducted an online survey of 500 consumers, randomly drawn from a customer database of 50,000, to assess brand perceptions.
Takeaways:
Well-designed sampling allows accurate conclusions about large populations from relatively small datasets when limitations of available data are accounted for. Non-random techniques undermine validity. Representative subsets yield helpful insights across varied applications in science, business and polling.