Fooled by Correlation: Common Misinterpretations in Social "Science"

March, 2019

Abstract

We present consequential mistakes in uses of correlation in social science research: 1) use of subsampling since (absolute) correlation is severely subadditive 2) misinterpretation of the informational value of correlation owing to nonlinearities, 3) misapplication of correlation and PCA/Factor analysis when the relationship between variables is nonlinear, 4) How to embody sampling error of the input variable 5) Intransitivity of correlation 6) Other similar problems mostly focused on psychometrics (IQ testing is infected by the "dead man bias") 7) How fat tails cause R2 to be fake. We compare to the more robust entropy approaches.