Sampling error consists of two components: sampling variance and sampling bias. Sometimes overall sampling error is referred to as sampling mean squared[Page 786]error (MSE), which can be decomposed as in the following formula:
where P is the true population value, p is the measured sample estimate, and p' is the hypothetical mean value of realizations of p averaged across all possible replications of the sampling process producing p.
Sampling variance is the part that can be controlled by sample design factors such as sample size, clustering strategies, stratification, and estimation procedures. It is the error that reflects the extent to which repeated replications of the sampling process result in different estimates. Sampling variance is the random component of sampling error since it results from "luck of ...
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