She argues that in much empirical analysis that people confuse statistical significance with substantive significance. In a play on words, she describes this as being the standard error of empirical analysis. For readers who are not statistically literate the standard error refers to the precision of the estimate that the analysis has produced. McCloskey argues that it isn't enough for an estimated coefficient to have a small standard error (i.e. be estimated with a high degree of precision) it must also have â€˜oomph'. I agree. So a highly statistically significant relationship might actually have a very small effect and so not be of substantive importance. So it's not really enough to just look at the statistical significance of any relationship, we also need to think about the size of the relationship. McCloskey talks about this in her book, joint with Stephen Ziliak, The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives and an entire issue of the 2004 Journal of Socio-Economics (subscription required) is dedicated to discussing the issue.
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