What distinguishes probability sampling from non-probability sampling?

Question

BlackTom AI · Accepted Answer

Probability sampling gives every member of the population a known, non-zero chance of being selected.

1. Relevant knowledge points and concepts
- Probability sampling: each unit in the population has a known, non-zero probability of selection. Common methods: simple random sampling, stratified sampling, cluster sampling, systematic sampling. Enables calculation of sampling error and confidence intervals.
- Non-probability sampling: selection is not based on random selection; examples include convenience, purposive, quota, and snowball sampling. Probabilities of selection are unknown, preventing unbiased estimators of sampling error.

2. Complete reasoning process step by step
- For a population of size N and a sample of size n drawn by simple random sampling, the probability that any particular individual is selected is n/N (a known, non-zero value).
- Because selection is random and probabilities are known, researchers can use probability theory to estimate sampling variability and construct confidence intervals.
- In non-probability methods, selection rules depend on researcher judgment or accessibility, so selection probabilities are unknown or zero for some units, invalidating standard inferential formulas.

3. Calculations / intermediate steps
- Example: population N = 1,000, desired sample n = 100 using simple random sampling. Probability any individual i is selected = n/N = 100/1000 = 0.10 (10%). This concrete numeric probability illustrates the key property.

4. Why the correct answer is correct
- The statement "Probability sampling gives everyone in the population a non-zero chance of being selected" encapsulates the defining property of probability sampling: known, non-zero selection probabilities for all units, enabling unbiased estimation and measurable sampling error.

5. Why the other options are incorrect
- "Probability sampling always produces a representative sample": incorrect — random samples can still be unrepresentative by chance, though they allow quantification of that risk.
- "Non-probability sampling is always biased": incorrect — it often introduces bias, but "always" is too strong; some non-probability designs can approximate representativeness in practice.
- "Probability sampling is generally easier than non-probability sampling": incorrect — probability sampling typically requires a sampling frame and more resources, so it is often more difficult.

6. Conclusion
The correct answer is: "Probability sampling gives everyone in the population a non-zero chance of being selected," which matches the provided answer.

What distinguishes probability sampling from non-probability sampling?单项选择题

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