Sampling

Definition

Sampling is the process of selecting a subset of individuals or items from a larger population to estimate characteristics of the whole population. It is crucial in statistics for making inferences about populations without examining every member.

5 Must Know Facts For Your Next Test

1. There are different types of sampling methods, such as simple random sampling, stratified sampling, and cluster sampling.
2. In simple random sampling, every member of the population has an equal chance of being selected.
3. Stratified sampling involves dividing the population into subgroups (strata) and then taking a sample from each stratum.
4. Cluster sampling involves dividing the population into clusters, randomly selecting some clusters, and then taking all members from those clusters.
5. Sampling error is the difference between the sample statistic and the actual population parameter.

Review Questions

• What is the main purpose of sampling in statistics?
• Describe the difference between stratified sampling and cluster sampling.
• What is sampling error?

Related terms

Population: The entire group that you want to draw conclusions about in a study.

Sample: A subset of individuals or items selected from a larger population for analysis.

Sampling Error: \text{The discrepancy between a sample statistic and its corresponding population parameter due to random selection.}