The HiTEA methodology is centered around three orthogonal statistical analysis frameworks, random forests, Z-score-ANOVA-Tukey (ANalysis Of VAriance), and PCA (Principal Component Analysis). Each framework answers one of the following questions: Which variable(s) are important? (random forest) , what are the statistically significant best-in-class/worst-in-class reagents (Z-score-ANOVA-Tukey) , and how do those best-in-class/worst-in-class reagents populate the chemical space (PCA) . Notably, this combination of statical analysis makes no assumption about the underlying data structure. For example, relationships can be non-linear or even discontinuous, the data does not need to be the full combinatorial cross of all reagents with all reactants, an important feature when considering the sparse nature of chemistry datasets, and smaller datasets are just as feasible as larger datasets. The synergy between these three branches of HiTEA paint a comprehensible understanding of a dataset's reactome, allowing for facile identification of hidden chemical insights. To highlight the flexibility and versatility of HiTEA, we analyze datasets that span upwards of 3,000 reactions across a broad range of substrates to datasets that are just over 1,000 reactions with a narrower substrate scope.