The case for default point-H1-hypotheses: 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, aka the true effect. Arriving at this estimate in evidence-based ways presupposes access to robust experimental or observational findings, defined as statistically significant test-results with high statistical power. In the behavioral sciences, however, even the best journals 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 engage with point-specific H0- and H1-hypotheses, behavioral scientists today typically point-specify only the H0, and instead engage a composite (directional) H1. This mismatch renders the prospects for theory-construction poor, because the population effect size—the very parameter that is to be modelled—regularly remains unknown. This can only keep from developing empirically adequate theoretical constructs. Based on the research program strategy (RPS), a sophisticated integration of Frequentist and Bayesian statistical inference elements, here we claim that theoretical progress requires engaging with point-H1-hypotheses by default.