We describe statistical modeling as a powerful alternative to null hypothesis significance testing (NHST). Modeling supports statistical inference in a fundamentally different way from NHST which can better serve developmental researchers. Modeling requires researchers to fully articulate their beliefs about the processes under study and to communicate that understanding through the structure of a probabilistic model before testing specific hypotheses. Research hypotheses are assessed through estimated parameters of the model and by conducting model comparisons. We conclude the paper with a series of worked examples that highlight the merits of the statistical modeling approach as a tool for scientific inference.