3D printing has enabled new avenues to design and fabricate diverse structures for engineering applications, such as mechanically efficient lattices. Lattices are useful as implants for biological applications for supporting in vivo loads. However, inconsistencies in 3D printing motivates a need to quantify uncertainties contributing to mechanical failure using probabilistic analysis. Here, 50 cubic unit cell lattice samples were printed and tested with designs of 50% porosity, 500-micron beam diameters, and 3.5mm length, width, and height dimensions. The average length, width, and height measurements ranged from 3.47mm to 3.48mm. The precision in printing with a 95% confidence level was greater than 99.8%. Lattice elastic moduli ranged from about 270 MPa to 345 MPa, with a mean of 305 MPa. Probabilistic analyses were conducted with NESSUS software. The distributions of input parameters were determined using a chi-square test. The first-order reliability method was used to calculate the probability of failure and sensitivity of each input parameter. The elastic modulus was the most sensitive among all input parameters, with 57% of the total sensitivity. The study quantified printing inconsistencies and sensitives using empirical evidence and is a significant step forward for designing 3D printed parts for mechanical applications.