The article discusses the use of dimension reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) in visualizing high-dimensional datasets, particularly in genetics and genomics. These tools are widely used to simplify complex data but can also introduce distortions and misleading results. Biostatistician Rafael Irizarry and other researchers highlight the importance of understanding the limitations and proper usage of these methods. They emphasize that while t-SNE and UMAP can be powerful for clustering and preserving local structures, they struggle with global structure preservation and can lead to spurious clusters. The article also addresses the need for careful parameter tuning and the importance of statistical rigor in data analysis. Researchers like Jingyi Jessica Li and Dmitry Kobak stress the need for justified parameter settings and the importance of considering the context and scientific questions when using these tools. The article concludes by advocating for a more thoughtful and methodical approach to using dimension reduction techniques, emphasizing the role of statistics in ensuring reliable and valid scientific conclusions.The article discusses the use of dimension reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) in visualizing high-dimensional datasets, particularly in genetics and genomics. These tools are widely used to simplify complex data but can also introduce distortions and misleading results. Biostatistician Rafael Irizarry and other researchers highlight the importance of understanding the limitations and proper usage of these methods. They emphasize that while t-SNE and UMAP can be powerful for clustering and preserving local structures, they struggle with global structure preservation and can lead to spurious clusters. The article also addresses the need for careful parameter tuning and the importance of statistical rigor in data analysis. Researchers like Jingyi Jessica Li and Dmitry Kobak stress the need for justified parameter settings and the importance of considering the context and scientific questions when using these tools. The article concludes by advocating for a more thoughtful and methodical approach to using dimension reduction techniques, emphasizing the role of statistics in ensuring reliable and valid scientific conclusions.