Rouder Jeffrey N, Speckman Paul L, Sun Dongchu, Morey Richard D, Iverson Geoffrey
University of Missouri, Columbia, MO 65211, USA.
Psychon Bull Rev. 2009 Apr;16(2):225-37. doi: 10.3758/PBR.16.2.225.
Progress in science often comes from discovering invariances in relationships among variables; these invariances often correspond to null hypotheses. As is commonly known, it is not possible to state evidence for the null hypothesis in conventional significance testing. Here we highlight a Bayes factor alternative to the conventional t test that will allow researchers to express preference for either the null hypothesis or the alternative. The Bayes factor has a natural and straightforward interpretation, is based on reasonable assumptions, and has better properties than other methods of inference that have been advocated in the psychological literature. To facilitate use of the Bayes factor, we provide an easy-to-use, Web-based program that performs the necessary calculations.
科学进步往往源于发现变量之间关系中的不变性;这些不变性通常对应于零假设。众所周知,在传统的显著性检验中无法陈述支持零假设的证据。在此,我们强调一种替代传统t检验的贝叶斯因子,它能让研究人员表达对零假设或备择假设的偏好。贝叶斯因子有自然且直接的解释,基于合理假设,并且比心理学文献中提倡的其他推理方法具有更好的性质。为便于使用贝叶斯因子,我们提供了一个易于使用的基于网络的程序来进行必要的计算。