Abstract
Modern statistical methods offer the quality assurance professional a powerful set of tools for the analysis of health care data. By using well-established procedures the quality assurance professional can isolate and quantify the relative degree of problems, determine statistical significance, and allocate resources towards problem resolution. This article briefly covers the concepts of hypothesis testing and statistical significance and their role in assisting the health care professional to assign priorities for use in problem resolution. Several statistical tests are described in the context of health care data analysis. The distinction between parametric, and non-parametric tests is made and a set of guidelines included on when and where the respective tests should be applied. An outline for the application of these statistical tests to several example situations is presented. Some details of population identification, sampling, and data organization are covered. Interpretation of results in the context of different problem situations is also covered.
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