Abstract

The curse of dimensionality is one of the most prominent challenge that data scientists face when trying to make valuable inferences. It is an epistemic problem that hits particularly hard in "Big Data" research contexts, where the volume of the data set is particularly large. The way in which we tackle with this problem sheds light on the notion of scientific evidence. Yet, it is virtually absent from the current philosophical literature. In this paper, I aim to broaden the focus of that literature by showing that the dimensions in which the data are embedded are an integral part of scientific evidence. This is an aspect of scientific evidence that is often hidden behind the traditional observation/theory dichotomy that we often encounter in philosophy of science. Dimensions are abstract objects. They are not observables like tables and chairs, nor are they entries in a data set. Ultimately, I aim to show that empirical adequacy is not merely a matter of finding the model that best fit the relevant observational data. It is also a matter of finding the model that best fit the relevant observational data inside the relevant dimensions.