Bayesian sample size planning for developmental studies

Abstract Running developmental experiments, particularly with infants, is often time‐consuming and intensive, the recruitment of participants hard expensive. Thus, an important goal for researchers to optimize sampling plans such that neither too many nor few are tested given hypothesis interest. One approach enables optimization use Bayesian sequential designs. The designs allows data collection be terminated as soon evidence deemed sufficiently strong, without compromising interpretability test outcome. In this tutorial, we illustrate how plan a testing design prior by method Bayes factor analysis—the equivalent power analysis—and discuss relevance psychologists. tutorial provides step‐by‐step guide perform analyses, methods illustrated using commonly used statistics in typical infant‐looking time paradigm can easily adapt these their studies. Highlights Sequential Testing sample sizes save on collection. Factor Design Analysis analyze study Step‐by‐step performing Analysis.