This article describes 3 relevant sampling situations that influ-
ence the design and analysis phases of a study and offers guidance for choosing the most effective and efficient design.
Situation 1: The study or activity involves taking a sample from a large finite target population for which enumerative
inferences are needed. Situation 2: The population is finite and the study is enumerative. A complete enumerative count
of “defects” in the process is needed so that remediation can occur. Here, statistical inference is unnecessary. Situation 3:
The target population is viewed as infinite; such populations are “conceptual populations” [1] or “processes”. Results:
The article shows how savings in resources can be achieved by choosing the correct analytic framework at the concep-
tualization phase of study design. Choosing the right sampling approach can produce accurate results at lower costs.
Several examples are presented and the implications for health services research are discussed. Conclusion: By clearly
specifying the objectives of a study and considering explicitly whether the data are a sample or a population, the practi-
tioner may be able to design a more efficient study and thereby conserve resources. This article provides a conceptual
framework in the form of three situations, several examples, and an algorithm (Figure 1) to help the intervention plan-
ner determine how to classify the study and when to apply the FPC.