There is a deep and well-regarded tradition in economics and other social sciences as well as in the physical sciences to assign causality to correlation analysis and statistical significance. I critique of critique the application of correlation analysis, unsubstantiated with any empirical backing of prior assumptions, as the core analytical measure for causation. Moreover, I present a critique of the past and current focus on statistical significance as the core indicator of substantive or analytical significance, especially well paired with correlation analysis. The focus on correlation analysis and statistical significance often results in analytical conclusions that are false, misleading, or spurious in terms of causality and analytical significance. This can generate highly misguided policy at an organizational, social, or even at a personal level. I also attempt explain the persistence of the misplaced use of these statistical techniques in the applied literature and propose a positive analytical frame wherein correlation analysis and tests of statistical significance, as part of a larger analytical toolbox, can make a positive contribution to the analytical literature.
|Number of pages||23|
|Journal||SSRN Working Paper Series|
|Publication status||Published - 28 Feb 2019|
- statistical significance
- correlation analysis
- behavioural economics
- mental models