Survey experiments often yield intention-to-treat effects that are either statistically and/or practically “non-significant.” There has been a commendable shift toward publishing such results, either to avoid the “file drawer problem” and/or to encourage studies that conclude in favor of the null hypothesis. But how can researchers more confidently adjudicate between true, versus erroneous, nonsignificant results? Guidance on this critically important question has yet to be synthesized into a single, comprehensive text. The present essay therefore highlights seven “alternative explanations” that can lead to (erroneous) nonsignificant findings. It details how researchers can more rigorously anticipate and investigate these alternative explanations in the design and analysis stages of their studies, and also offers recommendations for subsequent studies. Researchers are thus provided with a set of strategies for better designing their experiments, and more thoroughly investigating their survey-experimental data, before concluding that a given result is indicative of “no significant effect.”

A critical barrier to generating cumulative knowledge in political science and related disciplines is the inability of researchers to observe the results from the full set of research designs that scholars have conceptualized, implemented, and analyzed. For a variety of reasons, studies that produce null findings are especially likely to be unobserved, creating biases in publicly accessible research. While several approaches have been suggested to overcome this problem, none have yet proven adequate. We call for the establishment of a new discipline-wide norm in which scholars post short “null results reports” online that summarize their research designs, findings, and interpretations. To address the inevitable incentive problems that earlier proposals for reform were unable to overcome, we argue that decentralized research communities can spur the broader disciplinary norm change that would bring advantage to scientific advance. To facilitate our contribution, we offer a template for these reports that incorporates evaluation of the possible explanations for the null findings, including statistical power, measurement strategy, implementation issues, spillover/contamination, and flaws in theoretical priors. We illustrate the template’s utility with two experimental studies focused on the naturalization of immigrants in the United States and attitudes toward Syrian refugees in Jordan.