Bad results from good data

This review highlights some common errors of data evaluation that are frequently found in the literature. They include inappropriate choice of the model for fitting calibration curves, and usage of the correlation coefficient and linearization methods. We then question the notion about the advantage of non-selectivity of sensors in an array and highlight the danger of inadequate data-selection methods.