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On 17 June 2014, researcher Kiju Jung and colleagues published a study in the journal Proceedings of the National Academy of Sciences titled Female Hurricanes Are Deadlier Than Male Hurricanes. Using a combination of historical data and survey experiments, the study argued that more people die from feminine-sounding hurricanes because of an implicit bias that leads to the public to underestimate their risk relative to masculine-sounding hurricanes: The report used archival data from Atlantic hurricanes in the United States spanning 1950-2012. To quantify the clearly subjective view of what constitutes a masculine versus a feminine name, the study relied on ratings derived by nine individuals with no knowledge of the study’s hypothesis: Based on this analysis, the researchers concluded that the perceived femininity or masculinity of a name had no real bearing on milder storms, but that for severe ones (defined by how much damage they caused) the change was substantial, such that hurricanes with feminine names were much deadlier than those with masculine names. The crucial link underlying the researchers’ hypothesis, however, cannot be tested with that data alone, and therefore the researchers also conducted survey experiments to see if there was quantitative support for the idea that gender bias clouds people's view of the potential destruction of a hurricane. Their first experiment looked at expectations of hurricane severity based only on names: The second experiment gave 108 participants a map and a written scenario of a hurricane’s future possibilities and uncertainties. Each participant had the same information save the name of the Hurricane. One group's materials identified the fictional storm as Alexander, another Alexandra, and a control group for whom the storm was identified simply as hurricane. By their analysis, participants found hurricane Alexander riskier than Alexandra and the unnamed storm. The third and fourth experiments aimed to demonstrate the final link of their hypothesis: that perceived risk caused by implicit gender bias affects the likelihood of someone taking evacuation orders seriously (which subsequently contributes to the deadliness of the storm). Using similar survey methods, they reported that people were more likely to follow evacuation orders for a masculine sounding storm. As a whole, the researchers concluded: The study was widely and uncritically reported in a number of news outlets at the time, but as its stature as a viral story spread, so too did scrutiny of the methods utilized by the researchers, leading to calls that the conclusions were fatally flawed for two main reasons: 1) The study’s dataset included hurricanes from a time period (1950-1978) in which only female names were used; and 2) the statistical significance of the gendered trend relies, essentially, on only a small number of very deadly storms. The fact that only female storm names were used before 1979 complicates Jung’s results, statisticians have argued, because it makes it more likely that a deadly storm be given a female name. As discussed in a National Geographic story based on an interview with the National Centre for Atmospheric Research’s Jeff Lazo: Indeed, this fudge was pointed out in a response to the study also published in PNAS: The authors of the study responded to this line of criticism by saying that those analyses rely on a binary male or female gender, but noted that their study used perceived gender and presented that data as a degree of masculinity and femininity: They also noted that Lazo's claim of decreasing deadliness of hurricanes was not borne out by their analysis: The second complaint is the fact that any significant trend found in the data requires the presence of a few very deadly storms. Jung et al do note that there appears to be no trend for the less deadly storms, and that significance is only reached when adding a small number of very deadly storms. This, too, was noted in a separate published response in PNAS: By some analyses presented online, including the National Geographic piece, the entire trend's statistical significance could be changed if only one destructive storm — Hurricane Sandy — was not included. The authors of the study, however, challenge this finding as well, arguing that these interpretations were based on a simplistic statistical approach that was not in line with their research: Though originally presented by news outlets as a simple and shareable story, this study asked a simple question using extremely complex statistical tools that also involved myriad assumptions which have been challenged by a number of other researchers. Further, the mechanism (implicit bias) by which the researchers hypothesize their contested findings is only addressed indirectly, and requires agreeing with the conclusion of multiple online surveys. For these reasons, this seemingly simple question does not currently have a simple answer.
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