Recent years have witnessed calls for increased rigour and credibility in the cognitive and behavioural sciences, including psychophysiology. Many procedures exist to increase rigour, and among the most important is the need to increase statistical power. Achieving sufficient statistical power, however, is a considerable challenge for resource intensive methodologies, particularly for between-subjects designs. Meta-analysis is one potential solution; yet, the validity of such quantitative review is limited by potential bias in both the primary literature and in meta-analysis itself. Here, we provide a non-technical overview and evaluation of open science methods that could be adopted to increase the transparency of novel meta-analyses. We also contrast post hoc statistical procedures that can be used to correct for publication bias in the primary literature. We suggest that traditional meta-analyses, as applied in ERP research, are exploratory in nature, providing a range of plausible effect sizes without necessarily having the ability to confirm (or disconfirm) existing hypotheses. To complement traditional approaches, we detail how prospective meta-analyses, combined with multisite collaboration, could be used to conduct statistically powerful, confirmatory ERP research.

近年来，人们呼吁提高认知和行为科学（包括心理生理学）的严谨性和可信度。存在许多增加严谨性的程序，其中最重要的是需要增加统计能力。然而，获得足够的统计能力对于资源密集型方法是一个相当大的挑战，特别是对于主题间设计。Meta 分析是一种潜在的解决方案；然而，这种定量评价的有效性受到主要文献和荟萃分析本身的潜在偏见的限制。在这里，我们提供了对开放科学方法的非技术概述和评估，这些方法可用于提高新荟萃分析的透明度。我们还对比了可用于纠正主要文献中发表偏倚的事后统计程序。我们建议应用在 ERP 研究中的传统荟萃分析本质上是探索性的，提供一系列合理的效应大小，而不必确认（或否定）现有假设的能力。为了补充传统方法，我们详细介绍了如何使用前瞻性荟萃分析与多站点协作来进行具有统计学意义的验证性 ERP 研究。