Department of Psychology and Cognitive Sciences, University of Trento, Italy.
Department of Computer Science, Brunel University London, United Kingdom.
Infant Behav Dev. 2020 Nov;61:101483. doi: 10.1016/j.infbeh.2020.101483. Epub 2020 Oct 1.
Among infant researchers there is growing concern regarding the widespread practice of undertaking studies that have small sample sizes and employ tests with low statistical power (to detect a wide range of possible effects). For many researchers, issues of confidence may be partially resolved by relying on replications. Here, we bring further evidence that the classical logic of confirmation, according to which the result of a replication study confirms the original finding when it reaches statistical significance, could be usefully abandoned. With real examples taken from the infant literature and Monte Carlo simulations, we show that a very wide range of possible replication results would in a formal statistical sense constitute confirmation as they can be explained simply due to sampling error. Thus, often no useful conclusion can be derived from a single or small number of replication studies. We suggest that, in order to accumulate and generate new knowledge, the dichotomous view of replication as confirmatory/disconfirmatory can be replaced by an approach that emphasizes the estimation of effect sizes via meta-analysis. Moreover, we discuss possible solutions for reducing problems affecting the validity of conclusions drawn from meta-analyses in infant research.
在婴儿研究人员中，人们越来越关注广泛存在的研究实践，即研究样本量小，使用统计功效低（以检测广泛的可能效果）的测试。对于许多研究人员来说，信心问题可以通过依赖复制来部分解决。在这里，我们提供了进一步的证据，表明确认的经典逻辑，即复制研究的结果在达到统计学意义时确认原始发现，可以被有效地放弃。通过来自婴儿文献和蒙特卡罗模拟的真实示例，我们表明，在正式的统计意义上，非常广泛的可能复制结果可以构成确认，因为它们仅仅由于抽样误差就可以得到解释。因此，通常无法从单个或少数复制研究中得出有用的结论。我们建议，为了积累和产生新知识，可以用一种通过元分析强调效应大小估计的方法来替代复制作为确认/否定的二分法观点。此外，我们还讨论了可能的解决方案，以减少影响婴儿研究中从元分析得出结论的有效性的问题。
