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Compositional data, which represents parts of a whole (like percentages or proportions), presents unique challenges for statistical analysis that standard methods designed for real numbers often fail to address correctly. Despite a clear principle and sensible methodology being available for decades, inappropriate methods, confusions, and misunderstandings persist in many application areas. This leads to flawed or even meaningless results, particularly when applying standard multivariate analysis tools like product-moment correlation to compositional vectors. This paper delves into these ongoing issues, identifying common fallacies and confusions in compositional data analysis with illustrative examples. It critiques the use of traditional methods, including misleading graphical representations like Harker and ternary diagrams. By highlighting these prevalent errors and providing arguments for amendment, the paper aims to guide practitioners towards adopting the correct principle and methodology for analyzing compositional data, emphasizing that meaningful analysis must focus on the relative nature of the components. P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly Valls Geoconsultant ORCID ID- https://orcid.org/0000-0002-5421-0914 Scopus Author ID: 7003369619/35335510700 ResearcherID: S-6604-2018 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets