# Sampling Statistics by Wayne A. Fuller download in iPad, ePub, pdf

In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. Such results only provide a snapshot at that moment under certain conditions. Implementation usually follows a simple random sample. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population. However, in the more general case this is not usually possible or practical.

It is easy to implement and the stratification induced can make it efficient, if the variable by which the list is ordered is correlated with the variable of interest. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample. For example, suppose we wish to sample people from a long street that starts in a poor area house No.

Allows use of different sampling techniques for different subpopulations. These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory. Often there is large but not complete overlap between these two groups due to frame issues etc. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample or subset of that population.

Can be expensive to implement. Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone. Advantages over other sampling methods Focuses on important subpopulations and ignores irrelevant ones. Either stratified sampling or cluster sampling could be used.

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