Moving Beyondp < 0.05 in Ecotoxicology: A Guide for Practitioners

Statistical inferences play a critical role in ecotoxicology. Historically, null hypothesis significance testing (NHST) has been the dominant method for inference in ecotoxicology. As a brief and informal definition of NHST, researchers compare (or "test") an experimental treatment or observation against a hypothesis of no relationship (the "null hypothesis") using the collected data to see if the observed values are statistically "significant" given predefined error rates. The resulting probability of observing a value equal to or greater than the observed value assuming the null hypothesis is true is thepvalue. Criticisms of NHST have existed for almost a century and have recently grown to the point where statisticians, including the American Statistical Association (ASA), have felt the need to clarify the role of NHST andpvalues beyond their current common use. These limitations also exist in ecotoxicology. For example, a review of the 2010Environmental Toxicology & Chemistry(ET&C) volume that found many authors did not correctly reportpvalues. We repeated this review looking at the 2019 volume ofET&C. Incorrect reporting ofpvalues still occurred almost a decade later. Problems with NHST andpvalues highlight the need for statistical inferences besides NHST, something long known in ecotoxicology and the broader scientific and statistical communities. Furthermore, concerns such as these led the executive director of the ASA to recommend against use of "statistical significance" in 2019. In light of these criticisms, ecotoxicologists require alternative methods. We describe some alternative methods including confidence intervals, regression analysis, dose-response curves, Bayes factors, survival analysis, and model selection. Lastly, we provide insights for what ecotoxicology might look like in a post-pvalue world.Environ Toxicol Chem2020;39:1657-1669. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.