The purpose of this study was to assess the bias (objectivity) and variability (robustness) of computed tomography (CT) texture features (internal heterogeneities) across a series of image acquisition settings and reconstruction algorithms. We simulated a series of CT images using a computational phantom with anatomically-informed texture. 288 clinically-relevant simulation conditions were generated representing three slice thicknesses (0.625, 1.25, 2.5 mm), four in-plane pixel sizes (0.4, 0.5, 0.7, 0.9 mm), three dose levels (CTDIvol = 1.90, 3.75, 7.50 mGy), and 8 reconstruction kernels. Each texture feature was sampled with 4 unique volumes of interest (VOIs) (244, 1953, 15625, 125000 mm3). Twenty-one statistical texture features were calculated and compared between the ground truth phantom (i.e., pre-imaging) and its corresponding post-imaging simulations. Metrics of comparison included (1) the percent relative difference (PRD) between the post-imaging simulation and the ground truth, and (2) the coefficient of variation (%COV) across simulated instances of texture features. The PRD and %COV ranged from -100% to 4500%, and 0.8% to 49%, respectively. PRD decreased with increased slice thickness, in-plane pixel size, and dose. The dynamic range of results indicate that image acquisition and reconstruction conditions (i.e., slice thicknesses, in-plane pixel sizes, dose levels, and reconstruction kernels) can lead to significant bias and variability in texture feature measurements.