Some common misunderstandings in chemometrics

This paper describes a number of issues and tools in practical chemometric data analysis that are often either misunderstood or misused. Deciding what are relevant samples and variables, (mis‐)use of common model diagnostics, and interpretational issues are addressed in relation to component models such as PCA and PLS models. Along with simple misunderstandings, the use of chemometric software packages may contribute to the mistakes if not used critically, and it is thus a main conclusion that good data analysis practice requires the analyst to take responsibility and do what is relevant for the given purpose.