Final answer:
Sampling methods in statistics include stratified sampling, cluster sampling, systematic sampling, convenience sampling, and simple random sampling, each with distinct procedures and applications in research. These methods assist in selecting a subset from a population for study.
Step-by-step explanation:
Sampling is a fundamental concept in statistics that involves selecting a part of a population to represent the whole. Various sampling methods can be used depending on the study's aim and available resources.
Examples of Sampling Methods
- Stratified Sampling: Dividing the population into subgroups (strata) and selecting samples from each stratum proportionally.
- Cluster Sampling: Dividing the population into clusters, then randomly selecting entire clusters to study.
- Systematic Sampling: Choosing every kth individual from the population to sample.
- Convenience Sampling: Selecting individuals who are most readily available.
- Simple Random Sampling: Each member of the population has an equal chance of being selected.
Identifying Sampling Methods
In practice, every tenth person who enters a store might be polled, representing systematic sampling. When a computer generates random numbers to pick individuals, this is simple random sampling. Selecting participants based on convenience is known as convenience sampling, which, while easy to conduct, may not provide results representative of the entire population.