Reply to Christensen and Christensen and to Malter: Pitfalls of erroneous analyses of hurricanes names.

Our article (1) reports evidence for a relationship between femininity of hurricane names and fatality rates, along with experimental evidence that female-named storms elicit lower risk perceptions and preparedness intentions. In response, Malter (2) cites some bloggers’ critiques about inclusion of hurricanes from the era of female-only names, interpretation of results, and external validity of experiments. Christensen and Christensen (3) assert that the conclusions are invalidated by another significant interaction in the model. We show below that these critiques arise from inappropriate statistical treatments or other misunderstandings and do not qualify our findings. Malter (2) gives no compelling reason for excluding data before 1979 when only female names were given. Because we focused on name gender as a continuous variable, our modeling of 92 hurricanes appropriately included those data. To reiterate those results (1), modeling fatalities using a masculinityfemininity index (MFI) showed a significant interaction: for less damaging storms, MFI did not predict fatalities. However, for highly damaging storms, where taking protective action has the greatest impact on survival, a more feminine name predicted more fatalities. These findings do not appear to be explained by historical artifacts (e.g., more deaths during the female-only era due to inferior warning systems). We included years elapsed since the hurricane as a covariate, but it had no effect in any models (1). Even if we model only the data since 1979 (n = 54), we observe the same effect. [Note that, in the article, we neglected to apply the robust estimator to adjust extra SEs, recommended as the default standard error for count models (4–7). That adjustment, which affects the P values but not the coefficients, strengthens our conclusions.] The interaction between MFI and normalized damage that was reported as marginally significant (P = 0.073) is clearly significant with the recommended robust estimator (P = 0.004). It is also significant when examining name gender as a binary variable (P = 0.02). Although Christensen and Christensen (3) assert that “no effect can be observed” in these data that are significant “on a 10% level,” their analysis is simply incorrect. The focal interaction is clearly significant. In short, whether considering the full dataset or only storms since 1979, the femininity of hurricane names significantly predicts fatality rates for damaging storms. Christensen and Christensen (3) make more assertions based on improperly conducted analyses. Their critique of our figure 1 misconstrued its purpose. It is a method of visualizing the focal interaction by factoring normalized damage into high/low groups at its median (Fig. 1A). This illustrates changes in predicted fatalities at each MFI value without generating extreme predictions. It is not a test of the interaction. (Their assertion about the sign of the parameter is also mistaken.) Their subsequent analysis (3) excluding observations ≥100 deaths is also invalid and reflects a common misconception among researchers used to working with normal distributions. Hurricane fatality data are expected to be skewed. It is inappropriate to try to normalize the distribution by, for instance, arbitrarily deleting what one imagines are high counts (2, 3). Count models are specifically designed for such skewed datasets (5, 6). Christensen and Christensen (3) also assert that an extreme prediction of deaths for Hurricane Sandy undermines the model. However, A