Abstract

This paper motivates using explicit methods in linguistics by attempting to estimate the size of a linguistic data set. Such estimations are difficult because redundant data can easily pad the data set. To address this, I offer some explicit operationalizations of the data and their features. But for linguistic data, negative associations don’t indicate true redundancy, and yet for many measures they can be mathematically impossible to ignore. It is proven that this troublesome phenomenon has positive Lebesgue measure, is monotonically increasing, and that these two features hold robustly in four different ways