2020
DOI: 10.1002/aepp.13050
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Is There a Cult of Statistical Significance in Agricultural Economics?

Abstract: In an analysis of articles published in ten years of the American Economic Review, Deirdre McCloskey and Stephen Ziliak have shown that economists often fail to adequately distinguish economic and statistical significance. In this paper, we briefly review their arguments and develop a ten-item questionnaire on the statistical practice in the Agricultural Economics community. We apply our questionnaire to the 2015 volumes of the American Journal of Agricultural Economics, the European Review of Agricultural Eco… Show more

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Cited by 12 publications
(10 citation statements)
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“…Although the two authors witness some improvements over time (Ziliak and McCloskey, 2004) many problems prevail. As discussed by Rommel and Weltin (2021), similar problems are present in major agricultural economics journals.…”
Section: Emphasizing Economic Significance and Relevance With A Clear Distinction From Statistical Significancementioning
confidence: 64%
“…Although the two authors witness some improvements over time (Ziliak and McCloskey, 2004) many problems prevail. As discussed by Rommel and Weltin (2021), similar problems are present in major agricultural economics journals.…”
Section: Emphasizing Economic Significance and Relevance With A Clear Distinction From Statistical Significancementioning
confidence: 64%
“…The extremum value is Y opt=410,486. The extremum of the response function corresponds to the values of the factors: x2=0,08 (586) and x3=0,568 (98,4) for x1=1 (10). (Table 5).…”
Section: Methodsmentioning
confidence: 99%
“…At the same time, despite the widespread use of precision farming in the developed countries of the world, differentiated application of fertilizers and pesticides is carried out by ground equipment [9][10][11][12]. Equipment for light aircraft for differentiated application of mineral fertilizers and pesticides is under design decisions.…”
Section: Introductionmentioning
confidence: 99%
“…Gelman (2013), among others, argue that quantitative social scientists should not be particularly worried about committing Type I errors. Rather, we should be more concerned about Type S errors, getting the sign right, and Type M errors, getting the magnitude right (Gelman & Tuerlinckx, 2000;Rommel & Weltin, 2020;Ziliak & McCloskey, 2008). Pre-analysis plans are less useful in combating these type of errors.…”
Section: Reconciling Costs and Benefits Of Pre-analysis Plans In Economicsmentioning
confidence: 99%