Should I test more babies? Solutions for transparent data peeking

Abstract 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.