Statisticians and data analysts frequently have data sets which are difficult to analyze, with characteristics that break the underlying assumptions of the simpler analytical tools. But SAS® has tools to handle more complex problems. And, now that SAS has survey analysis procedures, there's no longer a need to pretend that survey samples should be analyzed using non-survey design tools. Now that large databases are commonly sampled for marketing, data mining, and scientific research, the need to use survey analysis procedures is increasing. In this paper, we'll look at several common situations and illustrate how the data are usually analyzed. Then we will show how the data ought to be analyzed using the SAS survey analysis procedures, and what the consequences of choosing the wrong procedures may be.
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