One of the first things one learns in a basic psychology or statistics course is that you cannot prove the null hypothesis that there is no difference between two conditions such as a patient group and a normal control group. This remains true. However now, thanks to ongoing progress by a special group of devoted methodologists, even when the result of an inferential test is p > .05, it is now possible to rigorously and quantitatively conclude that (a) the null hypothesis is actually unlikely, and (b) that the alternative hypothesis of an actual difference between treatment and control is more probable than the null. Alternatively, it is also possible to conclude quantitatively that the null hypothesis is much more likely than the alternative. Without Bayesian statistics, we couldn’t say anything if a simple inferential analysis like a t-test yielded p > .05. The present, mostly non-quantitative article describes free resources and illustrative procedures for doing Bayesian analysis, with t-test and ANOVA examples.

在基础心理学或统计学课程中学到的第一件事是，您无法证明两个条件（例如患者组和正常对照组）之间没有差异的零假设。这仍然是真实的。然而现在，由于一组专门的方法学家不断取得进展，即使推论检验的结果 p > .05，现在也可以严格和定量地得出以下结论：(a) 原假设实际上不太可能，并且(b) 治疗和控制之间实际差异的备择假设比原假设更有可能。或者，也可以定量地得出零假设比替代假设更有可能的结论。没有贝叶斯统计，如果像 t 检验这样的简单推论分析得出 p > .05，我们就无话可说了。目前，主要是非定量文章描述了进行贝叶斯分析的免费资源和说明性程序，包括 t 检验和方差分析示例。