[cwyers]

The money quote:

"But Lazo thinks that neither the archival analysis nor the psychological experiments support the team’s conclusions. For a start, they analysed hurricane data from 1950, but hurricanes all had female names at first. They only started getting male names on alternate years in 1979. This matters because hurricanes have also, on average, been getting less deadly over time. 'It could be that more people die in female-named hurricanes, simply because more people died in hurricanes on average before they started getting male names,' says Lazo.

Jung’s team tried to address this problem by separately analysing the data for hurricanes before and after 1979. They claim that the findings 'directionally replicated those in the full dataset' but that’s a bit of a fudge. The fact is they couldn’t find a significant link between the femininity of a hurricane’s name and the damage it caused for either the pre-1979 set or the post-1979 one (and a 'marginally significant interaction' of p=0.073 doesn’t really count). The team argues that splitting the data meant there weren’t enough hurricanes in each subset to provide enough statistical power. But that only means we can’t rule out a connection between gender and damage; we can’t soundly confirm one either."

[ghaff]

And BTW they just excluded Katrina as an outlier. While not necessarily a totally unwarranted decision, it makes already marginal conclusions that much more so.

[jaredsohn]

Isn't this conclusion more significant because Katrina is excluded?

Edit: I meant the layman's sense of "significant" here. I just don't see how excluding Katrina "makes already marginal conclusions that much more so". I read your "but" statement as just saying that they haven't necessarily proven causality, but I don't see what that has to do with my statement.

[cwyers]

Well, the word "significant" is a loaded one in this discussion -- in this context, it typically means "statistically significant," which has a very narrow (and sometimes questionable[1]) meaning.

You're right that leaving Katrina in the dataset (assuming the same techniques were used -- there are other ways to deal with outliers other than dropping them) would bias the result further towards indicating that female-named hurricanes are more dangerous. But the persistence of a significant finding in that regard in the absence of that data point does not prove a causal link, much less the specific one the author suggests.

[1] For some criticisms of how statistical significance is currently being used, read this: http://www.deirdremccloskey.com/docs/jsm.pdf

[ghaff]

It was intended more as a general point about picking and choosing of data. You're of course correct that including Katrina would buttress the female hurricane deadliness theory but would also open up the study to charges that it was influenced by a single outlier.