Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation
Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation
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贝叶斯数据分析：功率问题和零假设解释的新方法
DOI：
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发表时间：
2021
影响因子：
3
通讯作者：
J. Olson
中科院分区：
文献类型：
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作者：
J. Peter Rosenfeld;J. Olson
在基础心理学或统计学课程中，人们学到的第一件事是，你不能证明零假设，即两种情况(如患者组和正常对照组)之间没有区别。这仍然是事实。然而，现在，由于一组专注的方法学家正在取得进展，即使推论测试的结果是p >； .05，现在也可以严格地和定量地得出结论：(A)零假设实际上不太可能，以及(B)治疗和对照之间实际差异的替代假设比零假设更有可能。或者，也有可能从数量上得出这样的结论：零假设比另一种假设更有可能。如果没有贝叶斯统计，如果像t检验这样的简单推论分析得出p >； 0.05，我们就无话可说了。目前的这篇主要是非定量的文章描述了进行贝叶斯分析的免费资源和说明性程序，并以t检验和方差分析为例。
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.