 A more scientific approach to applied economics: Reconstructing statistical, analytical significance, and correlation analysisEconomic Analysis and Policy, 2020, vol. 66, issue C, 315-324 Abstract: There is a deep and well-regarded tradition in applied economic research and in other social sciences to assign causality to correlation analysis and statistical significance. I critique the application of correlation analysis, unsubstantiated with empirical backing of prior assumptions, as the core analytical measure for causation. Moreover, this paper critiques the past and current focus on statistical significance as the core indicator of substantive or analytical significance, especially when paired with correlation analysis. I attempt to explain the persistence of the misplaced use of these statistical techniques in the applied literature. I also propose a positive analytical frame wherein correlation analysis and tests of statistical significance, as part of a large analytical toolbox, can make a positive contribution to the analytical literature. Keywords: Statistical significance; Analytical significance; Correlation; Methodology; Mental models; Power; Decision making costs (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:66:y:2020:i:c:p:315-324 DOI: 10.1016/j.eap.2020.05.006 Access Statistics for this article Economic Analysis and Policy is currently edited by Clevo Wilson More articles in Economic Analysis and Policy from Elsevier |
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