Department of Methodology and Statistics, Faculty of Social Sciences, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.
Psychon Bull Rev. 2018 Apr;25(2):548-559. doi: 10.3758/s13423-018-1447-4.
This article explores whether the null hypothesis significance testing (NHST) framework provides a sufficient basis for the evaluation of statistical model assumptions. It is argued that while NHST-based tests can provide some degree of confirmation for the model assumption that is evaluated-formulated as the null hypothesis-these tests do not inform us of the degree of support that the data provide for the null hypothesis and to what extent the null hypothesis should be considered to be plausible after having taken the data into account. Addressing the prior plausibility of the model assumption is unavoidable if the goal is to determine how plausible it is that the model assumption holds. Without assessing the prior plausibility of the model assumptions, it remains fully uncertain whether the model of interest gives an adequate description of the data and thus whether it can be considered valid for the application at hand. Although addressing the prior plausibility is difficult, ignoring the prior plausibility is not an option if we want to claim that the inferences of our statistical model can be relied upon.
本文探讨了零假设显著性检验（NHST）框架是否为评估统计模型假设提供了充分的依据。本文认为，虽然基于 NHST 的检验可以在一定程度上证实所评估的模型假设（即零假设），但这些检验并不能告诉我们数据对零假设的支持程度，以及在考虑数据后，零假设应该在多大程度上被认为是合理的。如果目标是确定模型假设成立的可能性有多大，那么就必须解决模型假设的先验合理性问题。如果不评估模型假设的先验合理性，就完全无法确定所关注的模型是否对数据进行了充分的描述，因此也就无法确定该模型是否可用于当前的应用。尽管解决先验合理性问题具有一定的难度，但如果我们希望声称我们的统计模型的推断是可靠的，那么忽略先验合理性就不是一个可行的选择。
