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The expanded role of test demands a significant change in mind-set of nearly every engineer involved in the screening of semiconductor products. The issues to consider range from DFT and ATE requirements, to the design and optimization of test patterns, to the physical and statistical relationships of different tests, and finally, to the economics of reducing test time and cost. The identification of outliers to isolate latent defects will likely increase the role of statistical testing in present and future technologies. An emerging opportunity is to use statistical analysis of parametric measurements at multiple test corners to improve the effectiveness and efficiency of testing and reliability defect stressing. In this article, we propose a "statistical testing" framework that combines testing, analysis, and optimization to identify latent-defect signatures. We discuss the required characteristics of statistical testing to isolate the embedded-outlier population; test conditions and test application support for the statistical-testing framework; and the data modeling for identifying the outliers.