The case for default point-H1-hypotheses in behavioral science: a theory-construction perspective

The development of an empirically adequate theoretical construct for a given phenomenon of interest requires an estimate of the population effect size. Arriving at this estimate in evidence-based ways presupposes access to robust findings, defined as statistically significant test-results with high statistical power. In behavioral science, however, even the best journals today typically publish statistically significant test-results with insufficient statistical power, entailing that such findings have insufficient replication probability. Whereas a robust finding formally requires that an empirical study engages with point-specific H0- and H1-hypotheses, research in behavioral science typically point-specifies only the H0, and instead engages with a composite (directional) H1. This mismatch renders the prospects for theory-construction poor because the parameter to be theoretically modeled, the population effect size, regularly remains unknown, and so keeps from developing empirically adequate theoretical constructs. Against the background of the research program strategy (RPS), a sophisticated integration of Frequentist and Bayesian statistical inference elements, we claim theoretical progress in behavioral science requires engaging with point-H1-hypotheses by default.