The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.

在过去的十年里，人们对认知神经科学和更广泛的心理学的研究实践越来越关注，这动摇了我们对这些领域许多推论的信心。我们考虑这些问题如何影响发育认知神经科学，目标是推动我们的领域发展，以支持从我们的神经生物学数据中得出强有力的、站得住脚的推论。本手稿重点讨论了发展认知神经科学中区分验证性数据分析方法与探索性数据分析方法的重要性。关于验证性研究，我们讨论了分析灵活性、适当实例化假设以及根据我们如何阈值数据和纠正多重比较来控制错误率等问题。为了通过验证性分析来平衡这些担忧，我们提出了两种互补策略。首先，我们讨论在探索性分析框架内工作的优点，包括估计和报告效应大小、使用分区以及进行规格曲线分析。其次，我们总结了验证性分析中零假设显着性检验的合理方法，重点关注我们领域中透明和可重复的实践。只要有可能，就会给出具体的建议，并提供模板、脚本或其他资源的超链接。