Informative hypotheses: How to move beyond classical null hypothesis testing

In this dissertation I will show how subjective beliefs influence analyses in hidden ways and how they might be incorporated explicitly. I will argue that evaluating informative hypotheses produces more useful results than sequentially testing traditional null hypotheses against catch-all rivals. This is illustrated in the introduction chapter with an imaginary example of Aristotle’s investigations about the shape of the Earth. Then, I will take a philosophical approach with two chapters to the question of why one should evaluate informative hypotheses in the first place. Thereafter, I adopt a statistical perspective with three chapters and focus on the scope for extending the literature on the evaluation of informative hypotheses with some new tools. Finally, two applications in the field of developmental psychology are provided in which the research question is evaluated using informative hypotheses. All criticisms of classical null hypothesis testing aside, the best argument for evaluating informative hypotheses is probably that many researchers want to evaluate their expectation directly, but have been unable to do so because the statistical tools were not yet available. As this dissertation will illustrate, these tools are ready to be used for any researcher within the social sciences.