The practitioner must determine how the study will be
used. Classical analysis assumes that the data are a sim-
ple random sample. Clearly, this assumption is some-
times not met, especially in electronic full datasets (e.g.,
health care claims databases). Information in such data-
sets may sometimes be viewed as the entire population
(not a sample in any sense), although in some instances,
such data may be viewed as a “sample in time”, in which
case the data may be considered a simple random sample.
For this reason, resampling and hierarchical [16, p. 156]
models and other related techniques may become increas-
ingly attractive as alternative means of analyzing both
sparse and large full datasets
The practitioner must determine how the study will be used. Classical analysis assumes that the data are a sim- ple random sample. Clearly, this assumption is some- times not met, especially in electronic full datasets (e.g., health care claims databases). Information in such data- sets may sometimes be viewed as the entire population (not a sample in any sense), although in some instances, such data may be viewed as a “sample in time”, in which case the data may be considered a simple random sample. For this reason, resampling and hierarchical [16, p. 156] models and other related techniques may become increas- ingly attractive as alternative means of analyzing both sparse and large full datasets
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