applied to any HTE dataset to draw out hidden chemical insights is fundamental to driving forward data-driven chemistry. It is important to note that that this statistical framework was not envisioned to predict or generalize any specific reaction property (yield, selectivity, optimal conditions, etc.), but to provide a far more fundamental analysis: what are the chemical insights within a dataset? From these conclusions, we can begin to understand (a) what are statistically important factors that drive good or bad outcomes and (b) what this data will teach an AI model. Finally, comparison of the chemical insights embedded within the HTE data, what we dub the "HTE reactome", to the chemical insights drawn from the literature, the "literature's reactome", may (a) provide further evidence to support the mechanistic hypotheses (agreement of the reactomes), (b) reveal bias within the dataset which limits its usefulness or (c) reveal subtle correlations that may lead to refinement of our chemical understanding (disagreement of the reactomes) (Fig. ). For the purposes of this paper, "literature" is defined as information from open-source chemistry databases and published literature in peer-reviewed journals.