Statistical Issues in Defense Analysis and Testing Summary of a Workshop (1994) / Chapter Skim
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INTRODUCTION AND OVERVIEW
INTRODUCTION AND OVERVIEW
Pages 1-7
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The broad range of statistical issues that arise in the process of testing and evaluating major weapon systems prompted the Office of the Director for Operational Testing and Evaluation and the Office of the Assistant Secretary (now Director) for Program Analysis and Evaluation in the department to sponsor the workshop with the goal of improving the statistical methods used in DoD.
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COEAs are submitted or updated by the military services at the milestones in the acquisition process to evaluate the costs and benefits of new systems and their alternatives in terms of operational effectiveness in meeting defense needs. COEAs frequently include simulation experiments, analysis of which requires the use of appropriate statistical methods to make com parisons between alternatives.
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" workshop participants covered a gamut of statistical issues related to testing. CRITICAL PROBLEMS IN THE TESTING AND EVALUATION PROCESS The participants from the defense community described a wide range of issues and problems for which they thought statisticians could contribute ideas and possible solutions.
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These issues included trade-offs against alternative systems e.g., heavy antitank weapons and certain air defense weapons (that might also serve an antiarmor role) and the effects of battlefield countermeasures on the systems under consideration.
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Such curves are often used during acceptance sampling of continuously manufactured items to monitor product quality. One consequence of this sample size was that, within the structure of a statistical hypothesis test, the chances of decision errors were relatively high.
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Operational tests are currently viewed as starting with a blank slate. Statistical methods of combining information and borrowing strength across experiments (e.g., hierarchical Bayes and empirical Bayes models)
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Data are a precious resource in the Defense Department and could be used more effectively. Creating new capability in data archiving and management e.g., developing a relational data base coupled with a statistical and data analysis unit could improve the DoD's use of data.
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