In a weighted sample, not all sample observations contribute equally to the estimate of a population parameter.
Investigators are often interested in estimating quantities (such as means, counts, or proportions) in a population by using a representative sample selected from that population. Probability samples, defined as samples in which each sampling unit has a known, nonzero probability of selection based on the sampling design, allow investigators to compute estimates of population parameters. The most straightforward type of probability sampling design, a simple random sample (SRS), is a selection method in which each sample has the same probability of being selected. In an SRS, the probability of selection of each member in the population is the same.
References and Further Reading
- Foreman, E. K. (1991). Survey sampling principles. New York: M. Dekker.Google Scholar
- Kish, L. (1965). Survey sampling. New York: Wiley.Google Scholar
- Korn, E. L., & Graubard, B. I. (1995). Examples of differing weighted and unweighted estimates from a sample survey. The American Statistician, 49(3), 291–295.Google Scholar