Environmental forensic investigations often rely on large, complex chemical datasets that are difficult to interpret and even harder to explain clearly in a courtroom setting. Rather than evaluating individual chemical measurements in isolation, a multivariate statistical tool (Mike-MAP) allows these datasets to be reduced into simple visual representations that highlight meaningful patterns in the data. In this presentation, I demonstrate how multiple chemical measurements can be condensed into a single, intuitive figure that shows which samples are chemically similar and which are distinct, allowing experts to clearly communicate evidence related to source attribution and environmental impacts.The presentation emphasizes the importance of data exploration and transparency when applying statistical methods, particularly in legal contexts where different analytical approaches can produce different visual outcomes from the same dataset. By applying and comparing multiple dimensionality reduction techniques and evaluating the consistency and quality of the resulting patterns, this approach reduces the risk of over-interpretation and expert disagreement.Ultimately, this talk focuses on how complex chemical data can be responsibly simplified into defensible, easy-to-understand visuals that support clear expert testimony while explicitly acknowledging uncertainty. The goal is not to replace expert judgment with statistics, but to use statistics as a tool for clear, transparent communication of scientific evidence.