Die Interpretation des p-Wertes – Grundsätzliche Missverständnisse

Norbert Hirschauer, Oliver Musshoff, Grüner Sven, Frey Ulrich, Theesfeld Insa and Wagner Peter
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Grüner Sven: Professur für Unternehmensführung im Agribusiness, Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale)
Frey Ulrich: Professur für Agrar-, Umwelt- und Ernährungspolitik, Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale)
Theesfeld Insa: Professur für Agrar-, Umwelt- und Ernährungspolitik, Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale)
Wagner Peter: Professur für Landwirtschaftliche Betriebslehre, Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale)
Authors registered in the RePEc Author Service: Sven Gruener

Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 2016, vol. 236, issue 5, 557-575

Abstract: The p-value is often considered as the gold standard in inferential statistics. The standard approach for evaluating empirical evidence is to equate low p-values with a high degree of credibility and to refer to findings with p-values below certain thresholds (e.g., 0.05) as statistically significant. The p-value is also referred to as error probability. Both terms are problematic as they invite serious misconceptions. In addition, researchers’ fixation on obtaining statistically significant results may introduce biases and increase the rate of false discoveries. Misinterpretations of the p-value as well as the introduction of bias through arbitrary analytical choices (p-hacking) have been critically discussed in the literature for decades. Nonetheless, they seem to persist in empirical research and criticisms of inappropriate approaches have increased in the recent past – mainly due to the non-replicability of many studies. Unfortunately, the critical concerns that have been raised in the literature are not only scattered over many academic disciplines but often also linguistically confusing and differing in their main reasons for criticisms. Against this background, our methodological comment systematizes the most serious flaws and discusses suggestions of how best to prevent future misuses.

Keywords: Bayes theorem; hypothesis testing; error probability; p-hacking; statistical significance; Satz von Bayes; Hypothesentest; Irrtumswahrscheinlichkeit; p-hacking; statistische Signifikanz; Bayes theorem; hypothesis testing; error probability; p-hacking; statistical significance (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:jns:jbstat:v:236:y:2016:i:5:p:557-575

DOI: 10.1515/jbnst-2015-1030

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