Abstract | The safety of a nuclear power plant is influenced by both aleatory randomness and epistemic uncertainty, as well as the potential inter-component and intra-component correlations. Aleatory randomness arises from inherent variability in the data, while epistemic uncertainty stems from limitations, or incomplete knowledge in models or data. Component correlation refers to the extent to which the properties of various components within a Nuclear Power Plant (NPP) are interdependent and how they may co-vary within a single component (intra-component correlation) or among similar/identical ones (inter-component). Evaluating their effects to completion is a non-trivial operation that requires a full model of the power plant and its components, as well as the overall fault tree. When the goal is the evaluation of alternative approaches to safety assessment, one need not set the bar so high. In this, we offer a pared-down model, comprising simplified models of the reactor building and of one or more non-structural components, together with a simplified fault tree that leads to loss of core cooling capacity. As an example, three alternative cases of perfect, partial, and no correlation are employed to test common causes of failure. Uncertainty is propagated using a Monte Carlo simulation with either classic or progressive Latin hypercube sampling, using the simplified model as an efficient benchmark for NPP-compatible applications.