Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but not the former. This makes non-significant findings difficult to interpret: do they support the null hypothesis or are they simply not informative? Here we show how Bayesian hypothesis testing can be used in neuroscience studies to establish both whether there is evidence of absence and whether there is absence of evidence. Through simple tutorial-style examples of Bayesian t-tests and ANOVA using the open-source project JASP, this article aims to empower neuroscientists to use this approach to provide compelling and rigorous evidence for the absence of an effect.

大多数神经科学家都同意，为了使大脑研究取得进展，我们必须知道哪些实验操作没有影响，就像我们必须确定哪些实验操作确实有影响一样。神经科学中使用的主要统计方法依赖于P值，并且可以确定后者，但不能确定前者。这使得非显着的发现难以解释：它们是否支持零假设，或者它们根本没有提供信息？在这里，我们展示了如何在神经科学研究中使用贝叶斯假设检验来确定是否存在不存在的证据以及是否存在证据不存在。通过使用开源项目 JASP 进行贝叶斯t检验和方差分析的简单教程式示例，本文旨在使神经科学家能够使用这种方法为不存在影响提供令人信服且严格的证据。