  From the turbulent interstellar medium to the cosmic web, astronomers in many
different fields have needed to make sense of spatial data describing our
Universe, spanning centimetre to Gigaparsec scales. Through different
historical choices for mathematical conventions, many different subfields of
spatial data analysis have evolved their own language for analysing structures
and quantifying correlation in spatial data. Because of this history,
terminology from a myriad of different fields is used, often to describe two
data products that are mathematically identical. In this Note, we define and
describe the differences and similarities between the power spectrum, the
two-point correlation function, the covariance function, the semivariogram, and
the structure functions, in an effort to unify the languages used to study
spatial correlation. We also highlight under which conditions these data
products are useful and describe how the results found using one method can be
translated to those found using another, allowing for easier comparison between
different subfields' native methods. We hope for this document to be a
``Rosetta Stone" for translating between different statistical approaches,
allowing results to be shared between researchers from different backgrounds,
facilitating more cross-disciplinary approaches to data analysis.
