A More Scientific Approach to Applied Economics: Reconstructing Statistical, Analytical Significance, and Correlation Analysis

23 Pages Posted: 28 Feb 2018

Date Written: February 19, 2018

Abstract

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.

Keywords: methodology, statistical significance, correlation analysis, causality, behavioural economics, mental models

JEL Classification: B4, C4, D02, D7, Z13

Suggested Citation

Altman, Morris, A More Scientific Approach to Applied Economics: Reconstructing Statistical, Analytical Significance, and Correlation Analysis (February 19, 2018). Available at SSRN: https://ssrn.com/abstract=3126147 or http://dx.doi.org/10.2139/ssrn.3126147

Morris Altman (Contact Author)

University of Newcastle ( email )

University Drive
Callaghan, NSW 2308
Australia

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