In a study of successes and failures in adopting Common Core...

In a study of successes and failures in adopting Common Core standards, a random sample of high school principals will be selected from each of the 50 states. Selected individuals will be asked a series of evaluative questions. Why is stratification used here? (A) To minimize response bias (B) To minimize nonresponse bias (C) To minimize voluntary response bias (D) Because each state is roughly representative of the U.S. population as a whole (E) To obtain higher statistical precision because variability of responses within a state is likely less than variability of responses found in the overall population

In a study of successes and failures in adopting Common
Core standards, a random sample of high school principals
will be selected from each of the 50 states. Selected
individuals will be asked a series of evaluative questions.
Why is stratification used here?
(A) To minimize response bias
(B) To minimize nonresponse bias
(C) To minimize voluntary response bias
(D) Because each state is roughly representative of the U.S.
population as a whole
(E) To obtain higher statistical precision because variability
of responses within a state is likely less than variability of responses found in the overall population

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Key Concepts

Stratified Sampling

Stratified sampling is a sampling technique where the population is divided into distinct subgroups, or strata, that share similar characteristics. By selecting samples from each subgroup, researchers ensure that all relevant segments of the population are represented in the sample, which can lead to a more accurate and reliable estimation of overall parameters.

Statistical Precision

Statistical precision refers to the accuracy and consistency of the estimates produced by a sampling method. In stratified sampling, because the variability within each subgroup is usually lower than the variability in the overall population, the estimates tend to have lower error margins, resulting in higher precision.

Reduction of Population Variability

Reducing population variability involves minimizing the differences in responses within each subgroup compared to the total population variability. By sampling within more homogeneous strata, like individual states in this case, the overall variance of the estimate is decreased, leading directly to improved reliability and efficiency in the study's conclusions.

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