<p>In the nondestructive testing industry, probability of detection (POD) studies can be prohibitively expensive because of specimen and testing costs. In model-assisted probability of detection (MAPOD) analysis, physics-based model simulations are used to reduce the number of specimens needed to compute POD curves, but current MAPOD practices require precision simulations. These simulations need to have high enough accuracy to be treated as equivalent to real experimental data, perhaps after calibrating a “transfer function” that corrects their output. Not all simulators will have that level of accuracy, but when there are multiple inspection scenarios, the simulator accuracy can be assessed as part of the MAPOD process. This paper proposes Model-Quality-Adaptive POD (MoQuAPOD) which adapts to the demonstrated quality of a simulator across multiple inspection scenarios. The approach extends the traditional MAPOD concept of a simulation-to-experiment transfer function by acknowledging the uncertainty in its estimation. Stochastic transfer functions are evaluated and calibrated as a population over the range of inspection scenarios as part of the POD process. A hierarchical Bayesian model uses simulator predictions to draw strength across that population, reducing the number of specimens required to achieve a particular POD and confidence. We obtain POD estimates and population statistics from a Markov Chain Monte Carlo (MCMC) sampler. Population uniformity implies trustworthiness of the simulator, so that trustworthy simulators need less experimental data. An example illustrates model-quality-adaptive multi-scenario POD methods reducing the required number of specimens by 25%, compared to single-scenario POD methods, using synthetically generated data.</p>