Should I test more babies? Solutions for transparent data peeking

Research with infants is often slow and time-consuming, so infant researchers face great pressure to use the available participants in an efficient way. One strategy that researchers sometimes use to optimize efficiency is data peeking (or “optional stopping”), that is, doing a preliminary analysis (whether a formal significance test or informal eyeballing) of collected data. Data peeking helps researchers decide whether to abandon or tweak a study, decide that a sample is complete, or decide to continue adding data points. Unfortunately, data peeking can have negative consequences such as increased rates of false positives (wrongly concluding that an effect is present when it is not). We argue that, with simple corrections, the benefits of data peeking can be harnessed to use participants more efficiently. We review two corrections that can be transparently reported: one can be applied at the beginning of a study to lay out a plan for data peeking, and a second can be applied after data collection has already started. These corrections are easy to implement in the current framework of infancy research. The use of these corrections, together with transparent reporting, can increase the replicability of infant research.

婴幼儿研究往往耗时漫长，因此婴幼儿研究人员面临着在高效利用现有参与者方面的巨大压力。研究人员有时采取的一种优化效率的策略是数据预览（或称“选择性终止”），即对收集到的数据进行初步分析（无论是正式的显著性检验还是非正式的目测）。数据预览有助于研究人员决定是否放弃或调整研究，决定样本是否完整，或决定继续添加数据点。遗憾的是，数据预览可能带来负面影响，如错误阳性率上升（在不存在效应时错误地得出效应存在的结论）。我们认为，通过简单的校正，可以充分利用数据预览的益处，从而更高效地利用参与者。我们回顾了两种可以透明报告的校正方法：一种可以在研究初期应用于制定数据预览的计划，另一种可以在数据收集已经开始后应用。这些校正方法在婴幼儿研究当前框架下易于实施。这些校正方法与透明报告的结合，可以提高婴幼儿研究的可重复性。