Making sense of mineral trace-element data - How to avoid common pitfalls in statistical analysis and interpretation

Recent years have seen a sharp increase in the generation and use of mineral trace-element data in geological research. This is largely due to the advent of rapid and affordable laser-ablation inductively coupled plasma mass-spectrometry (LA-ICP-MS). However, while much new data is being generated and published, relatively little work has been done to develop appropriate methods for its statistical analysis and interpretation, and indeed, experimental design. In fact, several characteristic features of the data require careful consideration during evaluation and interpretation to avoid biased results. In particular, the commonly hierarchical structure of mineral trace-element data and its compositional nature must be taken into account to generate meaningful and robust results. Unfortunately, these features are not appropriately considered in most current studies. This review provides a general overview of the special features of mineral trace-element data and their consequences for statistical analysis and interpretation, as well as study design. Specifically, it highlights the need for 1) the use of log- or log-ratio-transformations for statistical analysis, 2) careful preparation of the raw data prior to analysis, including an appropriate treatment of missing values, and 3) the application of statistical methods suited to hierarchical data structures. These points, as well as the consequences of neglecting them, are illustrated with relevant examples from ore geology. However, the general principles described in this review also apply to mineral trace-element datasets collected in other fields of the geosciences, as well as other fields dealing with compositional data.