关于假设检验的争议:问题的澄清与解决

对传统的假设检验作为心理学实验的数据分析工具的评价涉及两个标准:首先是它是否合法,其次是它是否有用。置于频率学派统计学框架中的传统假设检验在逻辑上实际上是合法的;但它在效用性方面则有着备择假设不可证伪以及只能提供定性结论这两方面的缺陷。置信区间能够集中地改进和弥补这些缺陷。对传统假设检验使用中错误的澄清也使得研究者们开始重视PSI问题,这使得心理学实验的设计和数据分析从关注总体转向关注个体。     

新世纪20年国内假设检验及其关联问题的方法学研究

新世纪20年来国内假设检验方法学研究内容可分为如下几类: 零假设显著性检验的不足、p值的使用问题、心理学研究的可重复性问题、效应量、检验力、等效性检验、其他与假设检验关联的研究。零假设显著性检验已经发展成一套组合流程: 为了保证检验力和节省成本, 实验研究需要做先验检验力分析预估样本容量, 但问卷超过160人在传统统计中就没有必要这样做。当拒绝零假设时, 应当结合效应量做出结论。当不拒绝零假设时, 需要报告后验检验力; 如果效应量中或大而检验力不够高, 则可增加被试再行分析, 但这一过程应主动披露, 报告最后的实际p值并对可能犯的第一类错误率做出评估。     

心理研究中引入贝叶斯统计推断的必要性、应用思路与领域

本文从科学范式演变和概率统计推断特征分析出发,就贝叶斯统计推断引入心理学的必要性、怎么应用、在哪些领域应用集中进行了论述。首先在分析科学范式演进各阶段统计推断任务需要、经典的频率概率统计推断不足基础上,得出科学范式处于危机和革命阶段时需要对相关理论做可信度检验。然后,在介绍贝叶斯定理及其统计推断的基础上,进一步分析了贝叶斯推断能解决理论可信度的多种假设检验情景。最后本文还就贝叶斯统计推断在心理学理论争鸣与建构、心理技术产品开发与评估具体领域应用做了分析。     

Null hypothesis significance testing and Type I error: The domain problem

Although many common uses of p-values for making statistical inferences in contemporary scientific research have been shown to be invalid, no one, to our knowledge, has adequately assessed the main original justification for their use, which is that they can help to control the Type I error rate (Neyman & Pearson, 1928, 1933). We address this issue head-on by asking a specific question: Across what domain, specifically, do we wish to control the Type I error rate? For example, do we wish to control it across all of science, across all of a specific discipline such as psychology, across a researcher's active lifetime, across a substantive research area, across an experiment, or across a set of hypotheses? In attempting to answer these questions, we show that each one leads to troubling dilemmas wherein controlling the Type I error rate turns out to be inconsistent with other scientific desiderata. This inconsistency implies that we must make a choice. In our view, the other scientific desiderata are much more valuable than controlling the Type I error rate and so it is the latter, rather than the former, with which we must dispense. But by doing so—that is, by eliminating the Type I error justification for computing and using p-values—there is even less reason to believe that p is useful for validly rejecting null hypotheses than previous critics have suggested.