Naive Fault Trees (NFT) aim to extend the application of Fault Trees (FT) and make them appealing for system designers in the early project life cycle. NFT use input intervals and values to estimate the frequency of a top event. This extension facilitates the assignment of failure probability to basic events when exact data is difficult to find, unavailable or even not existent. The formulation of the problem and results are presented in this paper through an application to a real-world example. ed from different candidates contain ample information, which might not appear evident at first sight. The complexity of the situation requires an intelligent extraction of the information from the data. An analysis tool IndEvawas developed to handle this complexity and provide an accurate, detailed and reliable evaluation of inspection systems and personnel. Besides the plain evaluation regarding the fulfilment of the qualification requirements, critical test flaws as well as test block sections, which are likely to cause false positive indications can be identified. Statistic results display the dependency of the system performance on various parameters and parameter combinations to provide a clear picture of the performance. Country-specific evaluation standards can be applied and compared, especially with regard to the continuous improvement of the qualification methodology.