  Physical experiments in the national security domain are often expensive and
time-consuming. Test engineers must certify the compatibility of aircraft and
their weapon systems before they can be deployed in the field, but the testing
required is time consuming, expensive, and resource limited. Adopting Bayesian
adaptive designs are a promising way to borrow from the successes seen in the
clinical trials domain. The use of predictive probability (PP) to stop testing
early and make faster decisions is particularly appealing given the
aforementioned constraints. Given the high-consequence nature of the tests
performed in the national security space, a strong understanding of new methods
is required before being deployed. Although PP has been thoroughly studied for
binary data, there is less work with continuous data, which often in
reliability studies interested in certifying the specification limits of
components. A simulation study evaluating the robustness of this approach
indicate early stopping based on PP is reasonably robust to minor assumption
violations, especially when only a few interim analyses are conducted. A
post-hoc analysis exploring whether release requirements of a weapon system
from an aircraft are within specification with desired reliability resulted in
stopping the experiment early and saving 33% of the experimental runs.
