Biological systems are often modeled using computational methods such as finite element modeling because of the complex nature of the system being analyzed. However, most computational analyses fail to account for the variability and uncertainty of the model inputs and boundary conditions, which leads to an inability to predict a probability of injury in the given biological system. The goal of this study is to calculate the probabilistic response of a cervical spine finite element model by incorporating variability into the model inputs such as soft tissue properties and geometry. The geometry of the finite element model was created by using a set of geometry parameters that can be measured from Computed Tomography (CT) scans. The parameters were measured from CT scans of both male and female volunteers. Material properties for the soft tissues of the cervical spine were determined from literature and experimental data. Once the data was collected, random distributions were fit to both the geometry and material data. The software package NESSUS was used to calculate the probabilistic response of the cervical spine model. This methodology can be used to predict the probability of injury not only in the cervical spine but many other biological systems.