Strong-Form Frequentist Testing In Communication Science: Principles, Opportunities, And Challenges

This paper discusses 'strong-form' frequentist testing as a useful complement to null hypothesis testing in communication science. In a 'strong-form' set-up a researcher defines a hypothetical effect size of (minimal) theoretical interest and assesses to what extent her findings falsify or corroborate that particular hypothesis. We argue that the idea of 'strong-form' testing aligns closely with the ideals of the movements for scientific reform, discuss its technical application within the context of the General Linear Model, and show how the relevant P-value-like quantities can be calculated and interpreted. We also provide examples and a simulation to illustrate how a strong-form set-up requires more nuanced reflections about research findings. In addition, we discuss some pitfalls that might still hold back strong-form tests from widespread adoption.