Die Interpretation des p-Wertes - Grundsätzliche Missverständnisse

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.