 They conclude that big data analysis is by definition unable to distinguish spurious from meaningful correlations and is therefore a threat to scientific research. A related worry, sometimes dubbed “the curse of dimensionality” by data scientists, concerns the extent to which the analysis of a given dataset can be scaled up in complexity and in the number of variables being considered. It is well known that the more dimensions one considers in classifying samples, for example, the larger the dataset on which such dimensions can be accurately generalised. This demonstrates the continuing, tight dependence between the volume and quality of data on the one hand, and the type and breadth of research questions for which data need to serve as evidence on the other hand.