In this work, a synthetic dataset was constructed for molecular enthalpy at 298 K in units of kcal/mol as calculated from group additivity coefficients, based on the Benson group-increment theory . The dataset was desired to have characteristics well-suited to the analysis of errors of different types: no inherent noise, large dataset size, and a property function that was fully described by the features available to the model. With these characteristics, a model trained on the dataset could be driven to extremely low levels of noise, bias, and variance error. By starting from a dataset where very low error levels of all types are possible, the dataset can then be manipulated to elevate errors in a controlled way to exemplify situations in which model performance is dominated by the different types of error.