Decision-Based Metrics for Test and Evaluation Experiments
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We develop a new decision-based metric for determining sample sizes in Test and Evaluation experiments. Traditional confidence intervals for the mean can be used, and we present sequential confidence interval procedures as a way to derive efficient intervals. We discuss decision rules for analyzing the observed output and how to choose confidence interval methods for calibrating these decision rules. The metric presented can help determine if a fast decision on the quality of the system can be made or if many more tests are needed to ensure an accurate estimate of performance relative to a desired standard.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
NPS Report NumberSYM-AM-17-082
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