Abstract
Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.
Highlights
Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities
Correlations must be distinguished from theory-driven concepts such as prediction, causation, and determinism as being empirically prior to them
If correlations extend over time, they are called diachronic and express regularities such as dynamical laws within the same domain of discourse
Summary
In many areas of empirical science, one of the first steps to assess observed data is to identify and interpret patterns in these data. As big data science becomes ever more popular, most correlations detected as statistically significant in big datasets must be expected to be meaningless or irrelevant— the theory does not predict which ones. This sounds surprising, and is not generally known in big data science. Many correlation measures can be couched in terms of syntactic information such as Shannon information and variants thereof, various kinds of entropy, or algorithmic complexity All these quantitative measures are monotonic functions of randomness and are designed as context-free as possible, not to address questions of context or meaning The given literature refers either to seminal papers, in-depth reviews or monographs, or new and interesting developments
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call