Distinguishing Between Models and Hypotheses: Implications for Significance Testing

In the debate about the merits or demerits of null hypothesis significance testing (NHST), authorities on both sides assume that the p value that a researcher computes is based on the null hypothesis or test hypothesis. If the assumption is true, it suggests that there are proper uses for NHST, such as distinguishing between competing directional hypotheses. And once it is admitted that there are proper uses for NHST, it makes sense to educate substantive researchers about how to use NHST properly and avoid using it improperly. From this perspective, the conclusion would be that researchers in the business and social sciences could benefit from better education pertaining to NHST. In contrast, my goal is to demonstrate that the p value that a researcher computes is not based on a hypothesis, but on a model in which the hypothesis is embedded. In turn, the distinction between hypotheses and models indicates that NHST cannot soundly be used to distinguish between competing directional hypotheses or to draw any conclusions about directional hypotheses whatsoever. Therefore, it is not clear that better education is likely to prove satisfactory. It is the temptation issue, not the education issue, that deserves to be in the forefront of NHST discussions.