This article introduces HiTEA, a statistical analysis framework for high-throughput experimentation (HTE) data that enables the extraction of interpretable chemical insights from large-scale datasets. HiTEA is designed to analyze any HTE dataset, regardless of size or scope, and reveals statistically significant correlations between reaction components and outcomes. The framework combines three statistical methods: random forests, Z-score analysis of variance (ANOVA–Tukey), and principal component analysis (PCA), which together provide a comprehensive understanding of the chemical insights within a dataset. The authors validated HiTEA using cross-coupling and hydrogenation datasets, demonstrating its ability to uncover hidden relationships and highlight areas of dataset bias. The study also reveals that HiTEA can identify biases in HTE data, such as substrate selection bias, and provide insights into the chemical space that may require further investigation. The authors analyzed four reaction classes—Buchwald–Hartwig couplings, Ullmann couplings, heterogeneous hydrogenations, and homogeneous hydrogenations—each with a large number of reactions. For each, HiTEA identified key variables influencing reaction outcomes, such as solvent, base, catalyst, and temperature, and highlighted the importance of specific reagents and ligands. The analysis of the Buchwald–Hartwig dataset revealed that ligand electronic and steric properties significantly influence reaction yield, while the Ullmann dataset showed that certain ligands, such as phenanthroline-based and picolinamide-based ligands, are particularly effective. The hydrogenation datasets highlighted the importance of catalysts, temperature, and ligand structure in determining reaction outcomes. The study also identified areas where further research is needed, such as the role of specific ligands in asymmetric carbonyl reductions and the impact of solvent choice on reaction mechanisms. HiTEA's ability to identify biases in HTE data is crucial for improving the accuracy and generalizability of machine learning models in chemistry. The framework can be used to augment datasets with additional HTE data or to select subsets of data that are less biased. The authors conclude that HiTEA represents a significant step forward in the analysis of HTE data and encourages the chemical community to collect, publish, and analyze more HTE data to further explore the chemical reactome.This article introduces HiTEA, a statistical analysis framework for high-throughput experimentation (HTE) data that enables the extraction of interpretable chemical insights from large-scale datasets. HiTEA is designed to analyze any HTE dataset, regardless of size or scope, and reveals statistically significant correlations between reaction components and outcomes. The framework combines three statistical methods: random forests, Z-score analysis of variance (ANOVA–Tukey), and principal component analysis (PCA), which together provide a comprehensive understanding of the chemical insights within a dataset. The authors validated HiTEA using cross-coupling and hydrogenation datasets, demonstrating its ability to uncover hidden relationships and highlight areas of dataset bias. The study also reveals that HiTEA can identify biases in HTE data, such as substrate selection bias, and provide insights into the chemical space that may require further investigation. The authors analyzed four reaction classes—Buchwald–Hartwig couplings, Ullmann couplings, heterogeneous hydrogenations, and homogeneous hydrogenations—each with a large number of reactions. For each, HiTEA identified key variables influencing reaction outcomes, such as solvent, base, catalyst, and temperature, and highlighted the importance of specific reagents and ligands. The analysis of the Buchwald–Hartwig dataset revealed that ligand electronic and steric properties significantly influence reaction yield, while the Ullmann dataset showed that certain ligands, such as phenanthroline-based and picolinamide-based ligands, are particularly effective. The hydrogenation datasets highlighted the importance of catalysts, temperature, and ligand structure in determining reaction outcomes. The study also identified areas where further research is needed, such as the role of specific ligands in asymmetric carbonyl reductions and the impact of solvent choice on reaction mechanisms. HiTEA's ability to identify biases in HTE data is crucial for improving the accuracy and generalizability of machine learning models in chemistry. The framework can be used to augment datasets with additional HTE data or to select subsets of data that are less biased. The authors conclude that HiTEA represents a significant step forward in the analysis of HTE data and encourages the chemical community to collect, publish, and analyze more HTE data to further explore the chemical reactome.