Revision summary
Probability sampling: known chance of selection and estimable error. Simple random, systematic, stratified, cluster, and multistage PPS are the toolkit. NFHS and NSSO are Indian illustrations. A missing frame blocks the method. Quota and snowball are not probability designs.
Model answer
Introduction
Probability sampling gives every unit a known, non-zero chance of selection. It supports estimates of error. It is the design behind the Census sample surveys and NFHS, not behind a college queue.
Body
Simple random and systematic
- Simple random sampling draws from a complete frame, as in a lottery of listed households.
- Systematic sampling takes every k-th unit after a random start. It fails if the list has a hidden period.
Stratified and cluster
- Stratified sampling draws within sex, region, or rural–urban strata so rare groups are not missed. NFHS stratifies.
- Cluster sampling draws villages or blocks first, then households. It cuts cost and raises design effect.
Multistage and PPS
- Multistage combines clusters. The Indian NSSO and NFHS use stages from region to household.
- Probability proportional to size gives large villages a higher chance, then weights restore the estimate.
Example and limit
- A voter list can frame a city ward. Informal pavement workers often have no list, so probability design needs a mapped area sample.
Flow diagram
flowchart TD FRAME[Sampling frame] --> PR[Known chance] PR --> SRS[Simple random] PR --> ST[Stratified] PR --> CL[Cluster multistage] CL --> NFHS[NFHS NSSO]
Conclusion
Simple random, systematic, stratified, cluster, and multistage PPS are the main probability strategies. NFHS and NSSO illustrate them. Without a frame, the strategy cannot start. State the chance; do not call a quota a random sample.
Quick related
Students also ask
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According to Mead, "We play a key role in our own socialization."
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Is the full Census a sample?
The complete count is a census. Many Census tables also rest on samples of the long form.
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Why cluster if it is less precise?
Travel cost. Design weights and larger samples try to buy back precision.
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