We all care about effect sizes. Yet, traditional ways of evaluating them (P < 0.05 and generic benchmarks) are failing us. We propose two paths forward: setting better, contextualized benchmarks or — more radically — letting go of benchmarks altogether. Both paths point to adjusted expectations, more detailed reporting and slow science.

中文翻译：

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效应大小及其构成

我们都关心效应大小。然而，传统的评估方法（P < 0.05 和通用基准）却让我们失望。我们提出两条前进道路：设定更好的、符合实际情况的基准，或者更彻底地完全放弃基准。两条路径都指向调整的预期、更详细的报告和缓慢的科学。