The most common approaches to detection and attribution of extreme weather events using FAR or RR (Fraction of Attributable Risk or Risk Ratio) answer a particular form of research question, namely, “What is the probability of a certain class of weather events, given global climate change, relative to a world without?” In a set of recent papers, Kevin Trenberth et al. (2015) and Theodore Shepherd (2016) have argued that this is not always the best tool for analyzing causes, or for communicating with the public about climate events and extremes. Instead, they promote the idea of a “storyline” approach, which ask complementary questions, such as “How much did climate change affect the severity of a given storm?” From the vantage of history and philosophy of science, a proposal to introduce a new approach or to answer different research questions—especially those of public interest—does not appear particularly controversial. However, the proposal proved highly controversial, with the majority of detection and attribution scientists reacting in a very negative and even personal manner. Some suggested the proposed alternatives amount to a weakening of standards, or an abandonment of scientific method. Here, we address the question: Why is this such a controversial proposition? We argue that there is no “right” or “wrong” approach to D&A in any absolute sense, but rather that in different contexts society may have a greater or lesser concern with errors of a particular type. How we view the relative risk of over-estimation vs. under-estimation of harm is context-dependent.