The Epistemic Importance of Establishing the Absence of an Effect

In psychology as in many other sciences, Popperian rhetoric remains strong, even though Popperian practice has never been. Here, we provide an introduction to the four main approaches to epistemic justification, outlining the importance of null results in each and emphasizing the importance of each approach in developing a cumulative scientific literature. We argue that whether or not we subscribe to the Popperian Hypothetico-Deductive (HD) model of science, there is value in adopting Popper's advice about creating bold conjectures and risky tests for establishing the absence (or presence) of effects. However, the most popular approach to statistical testing, Null Hypothesis Significance Testing practice fails at both, and has arguably supported the censoring of null results from our scientific literature. Allowing null results into the scientific literature is essential for a cumulative science to function. However, we argue that even a repaired Popperian HD process won't offer much advice about what are interesting and important absences (or presences) to pursue. For answers to those fundamental questions, we need to appeal to other forms of epistemic justification such as those presented in this article

Backtracking and the Ethics of Framing: Lessons from Voles and Vasopressin

When communicating scientific information, experts often face difficult choices about how to promote public understanding while also maintaining an appropriate level of objectivity. We argue that one way for scientists and others involved in communicating scientific information to alleviate these tensions is to pay closer attention to the major frames employed in the contexts in which they work. By doing so, they can ideally employ useful frames while also enabling the recipients of information to “backtrack” to relatively uncontroversial facts and recognize how these frames relate to their own values and perspectives. Important strategies for promoting this sort of backtracking include identifying the weaknesses of particular frames, preventing misunderstanding of them, differentiating well-supported findings from more speculative claims, and acknowledging major alternative frames