Data from medical records are often limited in size, creating challenges for Artificial Intelligence training, and an opportunity for data augmentation techniques to create synthetic data. A natural question is how much data can be randomized and still be realistic. The main goal of this study is to attempt to measure the reproducibility of labs drawn in practice, so these ranges can be used for generating more realistic synthetic data. A second goal is to inform clinicians of the real error ranges of their laboratory data so these values may be better interpreted.