Reliability is an essential element in assessing the operational suitability of Department of Defense weapon systems. Reliability takes a prominent role in both the design and analysis of operational tests. In the current era of reduced budgets and increased reliability requirements, it is challenging to verify reliability requirements in a single test. Furthermore, all available data should be considered in order to ensure evaluations provide the most appropriate analysis of the system’s reliability. This paper describes the benefits of using parametric statistical models to combine information across multiple testing events. Both frequentist and Bayesian inference techniques are employed and they are compared and contrasted to illustrate different statistical methods for combining information. We apply these methods to data collected during the developmental and operational test phases for the Stryker family of vehicles. We show that, when we combine the available information across two test phases for the Stryker family of vehicles, reliability estimates are more accurate and precise than those reported previously using traditional methods that use only operational test data in their reliability assessments.
Suggested Citation
Steiner, Stefan, Rebecca M. Dickinson, Laura J. Freeman, Bruce A. Simpson, and Alyson G. Wilson. “Statistical Methods for Combining Information: Stryker Family of Vehicles Reliability Case Study.” Journal of Quality Technology 47, no. 4 (October 2015): 400–415. https://doi.org/10.1080/00224065.2015.11918142.