It has often been noted that the “null-hypothesis-significance-testing” (NHST) framework is an inconsistent hybrid of Neyman-Pearson’s “hypothesis testing” and Fisher’s “significance testing” that almost inevitably causes misinterpretations. To facilitate a realistic assessment of the potential and the limits of statistical inference, we briefly recall widespread inferential errors and outline the two original approaches of these famous statisticians. Based on the understanding of their irreconcilable perspectives, we propose “going back to the roots” and using the initial evidence in the data in terms of the size and the uncertainty of the estimate for the purpose of statistical inference. Finally, we make six propositions that hopefully contribute to improving the quality of inferences in future research.
