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U.S. Food and Drug Administration (FDA) recalls of medical devices are at historically high levels despite efforts by manufacturers to meet stringent agency requirements to ensure quality and patient safety. A factor in the release of potentially dangerous devices might be the interpretations of nonnormal test data by statistically unsophisticated engineers. The purpose of this study was to test the hypothesis that testing by lot provides a better indicator of true process behavior than process capability indices (PCIs) calculated from the mixed lots that often occur in a typical production situation. The foundations of this research were in the prior work of Bertalanffy, Kane, Shewhart, and Taylor. The research questions examined whether lot traceability allows the decomposition of the combination distribution to allow more accurate calculations of PCIs used to monitor medical device production. The study was semiexperimental, using simulated data. While the simulated data were random, the study was a quasiexperimental design because of the control of the simulated data through parameter selection. The results of this study indicate that decomposition does not increase the accuracy of the PCI. The conclusion is that a systems approach using the PCI, additional statistical tools, and expert knowledge could yield more accurate results than could decomposition alone. More accurate results could ensure the production of safer medical devices by correctly identifying noncapable processes (i.e., processes that may not produce required results), while also preventing needless waste of resources and delays in potentially life-savings technology, reaching patients in cases where processes evaluate as noncapable when they are actually capable.