In 2014, the National Osteoporosis Foundation reported that over half of the total adult population of the United States over the age of 50 suffers from osteoporosis or low bone mass. Osteoporosis is a degenerative bone disease which results in progressively compromised bone mechanical properties. The economic burden and increased risk of patient morbidity attributed to osteoporotic related fractures prioritizes early detection of a loss in bone mechanical properties. Methods used to evaluate bone mechanical properties vary widely depending on the motivation and environment of individual stakeholders. Further, the innate complexity of bone makes validation of each method difficult. Thus, the purpose of the present research was to evaluate methodological error and ease of implementation of the most common methods used to predict long bone stiffness. A catalog of the most common bone stiffness prediction methods and their limitations was generated from review of the literature. A bi-material and CT scan compatible bone surrogate was designed and fabricated for quantification of methodological error between experimental, analytical, and computational bone bending stiffness prediction methods. Micro-computed tomography (μ-CT) image-based analytical and finite element (FE) analyses were used to predict the mechanical and material properties of a murine femoral fracture healing model in order to characterize the capabilities and limitations of each approach. A novel high-performance voxel-based FEA approach capable of determining bone structural properties directly from medical image data was presented and evaluated for clinical applicability. The present research will inform stakeholders within research, clinics, and industry of the most appropriate bone stiffness prediction method for a given application.