论文标题
为什么要处理贝叶斯T检验？
Why bother with Bayesian t-tests?
论文作者
论文摘要
鉴于通过经典（点形）显着性测试进行假设检验的众所周知和基本问题，已经对替代方法进行了一般性，通常集中在贝叶斯t检验上。 We show that the Bayesian t-test approach does not address the observed problems with classical significance testing, that Bayesian and classical t-tests are mathematically equivalent and linearly related in order of magnitude (so that the Bayesian t-test providing no further information beyond that given by point-form significance tests), and that Bayesian t-tests are subject to serious risks of misinterpretation, in some cases more problematic than seen for classical tests （例如，在一个实验中，一个负样本的平均值提供了强有力的贝叶斯T检验证据，以支持阳性人群平均值）。我们不建议回到假设检验的经典，点形式的意义方法。取而代之的是，我们主张一种替代性分布方法来进行显着性测试，该方法解决了经典假设测试的观察到的问题，并在贝叶斯和频繁的方法之间提供了自然的联系。
Given the well-known and fundamental problems with hypothesis testing via classical (point-form) significance tests, there has been a general move to alternative approaches, often focused on the Bayesian t-test. We show that the Bayesian t-test approach does not address the observed problems with classical significance testing, that Bayesian and classical t-tests are mathematically equivalent and linearly related in order of magnitude (so that the Bayesian t-test providing no further information beyond that given by point-form significance tests), and that Bayesian t-tests are subject to serious risks of misinterpretation, in some cases more problematic than seen for classical tests (with, for example, a negative sample mean in an experiment giving strong Bayesian t-test evidence in favour of a positive population mean). We do not suggest a return to the classical, point-form significance approach to hypothesis testing. Instead we argue for an alternative distributional approach to significance testing, which addresses the observed problems with classical hypothesis testing and provides a natural link between the Bayesian and frequentist approaches.
