  Given a dataset we quantify how many patterns must always exist in the
dataset. Formally this is done through the lens of Ramsey theory of graphs, and
a quantitative bound known as Goodman's theorem. Combining statistical tools
with Ramsey theory of graphs gives a nuanced understanding of how far away a
dataset is from random, and what qualifies as a meaningful pattern.
  This method is applied to a dataset of repeated voters in the 1984 US
congress, to quantify how homogeneous a subset of congressional voters is. We
also measure how transitive a subset of voters is. Statistical Ramsey theory is
also used with global economic trading data to provide evidence that global
markets are quite transitive.
