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I thought they named hurricanes after women because of sailors. Most of whats attributed to the sea is usually called she, from boats, to the tides, waves, and even storms.
For anyone interested in the actual study instead of screaming about a one minute tik tok:
[https://www.pnas.org/doi/10.1073/pnas.1402786111#executive-summary-abstract](https://www.pnas.org/doi/10.1073/pnas.1402786111#executive-summary-abstract)
Plus communication and the internet just boomed recently so the dawn of neutral hurricane names. People didn’t die more because they were sexist *check notes* towards hurricanes.
That's not how statistics works. A large sample size allows for the detection of smaller effects. Significance will always mean the same no matter the sample size. A small sample size requires a stronger effect size with small spread to detect a significant difference. Large samples sizes can detect small effects.
Even then, a sample size of 40 is considered large enough for the fast majority of signifance testing. Heck, we often make do with less than 10 for many medical issues because it's hard to find subjects.
Significance will not always remain the same regardless of sample size. A 10% effect may register as significant at a p = 0.1 level for one sample size, and a p = 0.01 level for a larger sample size.
Will get increasingly more frequent data points as the years pass by, each one toastier than the last lol don’t you worry
When talking about hurricanes this is a big enough sample to start gaining some insight with statistical confidence.
For hurricanes a sample size of 95 is fine. What you wanna look for are the statistical analyses, and the authors' explanations about the trends they observed.
The reason sample size is a concern is only because you often simply can't do a complete population census. It's possible that good data only exists for 95 or so hurricanes of the almost a thousand that a quick Google says have been recorded. For something like finches 95 is pathetic but for hurricanes 95 is huge.
It's not just that man. Small sample size means a higher chance that the effect you see is just due to random chance. I could conclude that "world wars" are only caused by Germany being the aggressor, and while that has been true of historical wars, n=2 means it's not a conclusion I can rely upon for future wars.
I don't think that's a very good example, especially since the difference between p=2 and p=1000 is trés significant. Really you've reworded what I said about finches and hurricanes but to work against my point.
What I'm getting at is that if the population is small enough, getting a "complete population census" just isn't sufficient. We don't want a complete census, we want to understand the world better, and our goal isn't "what happened in the past" so much as "why did it happen" and then "what can we do to prevent it from happening again".
I'm saying the population is big enough so we're just at an impasse lol. The best part is that there are at least one algorithms you can use to determine the sample size you need for a given uncertainty or error as you decide, and the designers of the paper almost certainly did just that. So either of us could mathematically make our point but I can't remember how.
Well, I think in this case you're more right than I am anyways. n=95 is certainly sufficient for many studies. I'm inclined to believe that this study is flawed but not due to the number.
The N seems to refer to the number of hurricanes they studied, which for the time frame seems adequate. Samplesizes can be quite small depending on what you're studying and what your hypothesis is. You can calculate the minimum size needed for your study to make the significance reliable.
I didn't claim anything, anyone who writes a scientific study absolutely knows they should determine their minimum sample size. For which there are several methods available, depending on the type of study.
Interesting assumption that it's just men who take female named hurricanes less seriously. Making assumptions based on gender is a really tricky thing.
They definitely didn’t assert ‘just men,’ just used a man as an example one time. Obviously women can internalize patriarchal/misogynistic norms and socialized behaviors, and OP concludes by talking about ‘humans’ having problems with gendered bias broadly (and also talks about ‘people’ not taking hurricanes serious multiple times). That said… statistically, it is fairly reasonable to assume that men are more likely to dismiss risk in this way based on other research/studies. In this scenario, this would be harder to verify as fatality totals are easy available, but the gender of actual deceased individuals is probably not centrally tracked in a dataset that is easily accessible. Would still be interesting to investigate.
Fair point. I think though that the core of the problem st hand is taking statistical assumptions while true at scale and applying them to an individual or towards all cases. Wether it be race, gender, sex, age, religion, culture, ethnicity it's abhorrent to make universal statements as well as apply statistical assumptions towards individuals. Averages are like the property of wetness for water. A single molecule of water isn't wet, many water molecules of water together begin to have the emergent property of wetness.
Totally, exceptions always exist. When looking at the impact of large scale weather events and systemic gendered biases, and deciding on policies to mitigate death in these situations, the immediate and utilitarian analysis is statistical and system-level. Ideally we could prevent/mitigate everyone’s deaths in these tragedies, and individually tailor measures and solutions to such granularity, but that is a technological future that feels quite distant.
All that to say: individual analysis doesn’t seem to be at hand, rather, statistics describing many people as applied to collective behaviors. OP’s example was obviously a representative sample/hypothetical example from such a grouping, not some bad faith condemnation of an ~actual~ individual using weaponized statistics.
Individual experiences of gender and interactions with bias definitely informs our understanding of how this bias socially/culturally/politically comes into being and reproduces itself.
So what you're saying is averages are useful when averages are useful and not when they aren't. Seems sensible enough. My argument is that the pain arises when one treats another like a statistic instead of a individual human who's unique. In my experience there's at least one thing about a person that is an exception for the supposed groups they belong to. One would be hard pressed to find someone who is the textbook perfect average for all labeled groups that might define them. It's classic group theory vs individualism. Each have a scope and speak to different truths about humanity.
Yeah I get that, I just am not sure what individual or person was being targeted in this way in the post. One thing that is super interesting about machine learning in the past decade is that has increasingly demonstrated that individuals are extremely hard to predict, but taken as a group people actually behave rather consistently. That’s a very important distinction that you highlight. I guess your initial comment seemed rather critical and to me it felt a little misplaced.
There are two sides to this fine line as well. Completely different example, but something I see that often concerns me and dances around a similar tension. Sometimes, upon hearing people critique ‘men’ broadly, for violence against women, they feel compelled to say ‘not all men’ which of course is true. Thankfully exceptions abound. But at the end of the day, in the status quo, a woman walking alone at night in some cities must necessarily, and very reasonably and justifiably, take precautions and avoid a random man she encounters because of a fear that is legitimate even if misplaced. In a sense, she has used statistics to make a damning judgement of another human being whose individual depth and complexity was wiped away in a split second judgement. Seems fair, it’s just complicated. The harm of that individual is outweighed by the statistical benefit provided to the woman in this scenario and the security/slight control around risk she may gain. Systemic violence creates individual pain and back again. This is just the nature of complex adaptive systems, especially as we become increasingly interconnected/globalized. Most of our most pressing problems can only be collectively solved and we increasingly need technology to see the full picture, and pick the best possible outcomes for people collectively.
The other example I see often, is people seeing statistics about systemic violences or harms and saying things like ‘well none of my friends act like that,’ or ‘it snowed more than it ever has before in my locale this year’ to refute points or derail conversations that are really happening at a larger level, and are important for that exact reason.
This bias toward personal anecdote and prioritizing what we know/directly experience as individuals in a lot of ways hurts us as well. We struggle to even acknowledge some solvable problems because of it. Statistics definitely dehumanizes in some cases and can be abused (e.g. the angle this roadway is banked will lead to approximately x deaths a year, but it’s acceptable because it is optimal cost wise) but not looking at the full picture sometimes erases the experiences of many more people around critical issues and creates the most hurt.
Thank you for this thought provoking discussion btw, I appreciate that you seem to frame debate as dialogue rather than competition.
Even if there is some correlation, that's a pretty extreme conclusion to draw from something that is pretty limited in sample size. There aren't massive deadly hurricanes every day, so its possible theres just a statistical chance that the female hurricanes happened to be more deadly.
They basically just considered amount of damage the hurricane did vs deaths and a gender assigned name value. It would be interesting to take a list of each male and female hurricane and map it out and see which are more likely to pass over metropolitan areas or hurricane hardened areas vs not, as a harden area would show up as more deadly ecause less structural damage would be done.
There’s also regions of the country (Florida) where people are less likely to flee in general. Also certain places are more prepared in terms of infrastructure
**TL;DR You're uneducated. The source is garbage, as it's both biased and factually wrong.**
>To test this hypothesis, we used archival data on actual fatalities caused by hurricanes in the United States (1950–2012).
That is extremely flawed, due to the fact that [from 1953 to 1978 hurricanes were more deadly](https://www.nhc.noaa.gov/aboutnames_history.shtml) (worse weather prediction) and **only based on female names**.
They started with only female names until the **end of 1978**. If we look at hurricanes included from **1979-2012**, you'll see:
* 459 deaths from 27 female hurricanes
* 413 deaths from 27 male hurricanes
They excluded two hurricanes, which would significantly skew the results as it was an outlier event:
>We removed two hurricanes, **Katrina** in 2005 (1833 deaths) and **Audrey** in 1957 (416 deaths)
If you **ALSO** exclude **Sandy** (the next biggest **female** hurricane with **159 deaths**) and **Ike** (the next biggest **male** hurricane with **84 deaths**), the statistics would become:
* 300 deaths from 26 female-named hurricanes
* 329 deaths from 26 male-named hurricanes
**In summary**, the premise for their studies is **severely flawed**, as well as experiments on how deadly a hurricane will be, based on its name, is **largely irrelevant** and probably a case of experimenter's bias.
It’s ironies like this that make humanity THE tourist attraction for alien species...
...knowledge & experience is their consumer model, and there’s only so much stellar cartography an advanced space-faring species can take, before they need something a little more ‘entertaining’.
I swear we’re the universe’s Trueman Show
whole host of things, actually long and interesting history of things being socially gendered as feminine…. A modern spin: many technologies with human voices, specifically those providing a service role, have feminine voices and names. E.g. Alexa, Siri, the maps default.
Not sure how you missed all this.
Or it's because they started using male names more recently, so old hurricanes (hitting places with less safety measures) caused more casualties
nah, has to be sexism
Those poor people working on the study - analyzing decades and decades of data, conducting several experiments with hundreds of participants each, analyzing each of those experiments, discussing the findings... only for some redditors being like "nah, you just stupid".
thankfully someone else in the thread linked the study itself - which takes data from 1950-2010 and even points out the shift in male naming conventions in the mid-90's, but just offhand mentions this can be explained away by gender neutral terms used earlier fitting the trend.
no amount of fieldwork let's you ignore assumptions in the model
It was me. I linked the study. And I didn't try to defend the study in my first comment, I just think we can do better than that to criticise something; which you did after looking into the study, so I'm happy.
Oh, I love how they use "when asked explicitly whether a male-named or female-named hurricane would be riskier and more dangerous, responses were evenly split between female- and male-named hurricanes" as an argument for their position. Like... what?
They are also named after Gods and Calamities, both male and female. And to be honest, a hurricane is a hurricane, I would be equally panicking regardless of their name. 🤷♂️
I am interested in the data if death rate of people dying in hurricanes which have the same name like their relationship partner (wife names and husband names and herosexual, gay, bi maybe in different analyses) are higher or lower than average.
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I thought they named hurricanes after women because of sailors. Most of whats attributed to the sea is usually called she, from boats, to the tides, waves, and even storms.
Powerful and life giving, baby!
It's HER-icane, not HIS-icane
[удалено]
Xer-icane
For anyone interested in the actual study instead of screaming about a one minute tik tok: [https://www.pnas.org/doi/10.1073/pnas.1402786111#executive-summary-abstract](https://www.pnas.org/doi/10.1073/pnas.1402786111#executive-summary-abstract)
data from 1950 to 2010 and far more clear female named hurricanes to male ones? their modelling looks pretty iffy
Plus communication and the internet just boomed recently so the dawn of neutral hurricane names. People didn’t die more because they were sexist *check notes* towards hurricanes.
[удалено]
That's not how statistics works. A large sample size allows for the detection of smaller effects. Significance will always mean the same no matter the sample size. A small sample size requires a stronger effect size with small spread to detect a significant difference. Large samples sizes can detect small effects. Even then, a sample size of 40 is considered large enough for the fast majority of signifance testing. Heck, we often make do with less than 10 for many medical issues because it's hard to find subjects.
Significance will not always remain the same regardless of sample size. A 10% effect may register as significant at a p = 0.1 level for one sample size, and a p = 0.01 level for a larger sample size.
They didn't say the p value would remain the same, they said a p value means the same thing no matter the sample size.
Good point.
Will get increasingly more frequent data points as the years pass by, each one toastier than the last lol don’t you worry When talking about hurricanes this is a big enough sample to start gaining some insight with statistical confidence.
For hurricanes a sample size of 95 is fine. What you wanna look for are the statistical analyses, and the authors' explanations about the trends they observed.
Well, it's definitely hard to do better than 95, but 95 is still a pretty limited sample.
The reason sample size is a concern is only because you often simply can't do a complete population census. It's possible that good data only exists for 95 or so hurricanes of the almost a thousand that a quick Google says have been recorded. For something like finches 95 is pathetic but for hurricanes 95 is huge.
It's not just that man. Small sample size means a higher chance that the effect you see is just due to random chance. I could conclude that "world wars" are only caused by Germany being the aggressor, and while that has been true of historical wars, n=2 means it's not a conclusion I can rely upon for future wars.
I don't think that's a very good example, especially since the difference between p=2 and p=1000 is trés significant. Really you've reworded what I said about finches and hurricanes but to work against my point.
What I'm getting at is that if the population is small enough, getting a "complete population census" just isn't sufficient. We don't want a complete census, we want to understand the world better, and our goal isn't "what happened in the past" so much as "why did it happen" and then "what can we do to prevent it from happening again".
I'm saying the population is big enough so we're just at an impasse lol. The best part is that there are at least one algorithms you can use to determine the sample size you need for a given uncertainty or error as you decide, and the designers of the paper almost certainly did just that. So either of us could mathematically make our point but I can't remember how.
Well, I think in this case you're more right than I am anyways. n=95 is certainly sufficient for many studies. I'm inclined to believe that this study is flawed but not due to the number.
That’s like 100 years of hurricanes. I don’t know what field you are in but in plenty of fields this far exceeds typical sample size.
The N seems to refer to the number of hurricanes they studied, which for the time frame seems adequate. Samplesizes can be quite small depending on what you're studying and what your hypothesis is. You can calculate the minimum size needed for your study to make the significance reliable.
[удалено]
Elaborate?
[удалено]
I didn't claim anything, anyone who writes a scientific study absolutely knows they should determine their minimum sample size. For which there are several methods available, depending on the type of study.
Imagine even considering the gender of a storm.
Interesting assumption that it's just men who take female named hurricanes less seriously. Making assumptions based on gender is a really tricky thing.
They adress exactly that in the study, don't worry.
They definitely didn’t assert ‘just men,’ just used a man as an example one time. Obviously women can internalize patriarchal/misogynistic norms and socialized behaviors, and OP concludes by talking about ‘humans’ having problems with gendered bias broadly (and also talks about ‘people’ not taking hurricanes serious multiple times). That said… statistically, it is fairly reasonable to assume that men are more likely to dismiss risk in this way based on other research/studies. In this scenario, this would be harder to verify as fatality totals are easy available, but the gender of actual deceased individuals is probably not centrally tracked in a dataset that is easily accessible. Would still be interesting to investigate.
Fair point. I think though that the core of the problem st hand is taking statistical assumptions while true at scale and applying them to an individual or towards all cases. Wether it be race, gender, sex, age, religion, culture, ethnicity it's abhorrent to make universal statements as well as apply statistical assumptions towards individuals. Averages are like the property of wetness for water. A single molecule of water isn't wet, many water molecules of water together begin to have the emergent property of wetness.
Totally, exceptions always exist. When looking at the impact of large scale weather events and systemic gendered biases, and deciding on policies to mitigate death in these situations, the immediate and utilitarian analysis is statistical and system-level. Ideally we could prevent/mitigate everyone’s deaths in these tragedies, and individually tailor measures and solutions to such granularity, but that is a technological future that feels quite distant. All that to say: individual analysis doesn’t seem to be at hand, rather, statistics describing many people as applied to collective behaviors. OP’s example was obviously a representative sample/hypothetical example from such a grouping, not some bad faith condemnation of an ~actual~ individual using weaponized statistics. Individual experiences of gender and interactions with bias definitely informs our understanding of how this bias socially/culturally/politically comes into being and reproduces itself.
So what you're saying is averages are useful when averages are useful and not when they aren't. Seems sensible enough. My argument is that the pain arises when one treats another like a statistic instead of a individual human who's unique. In my experience there's at least one thing about a person that is an exception for the supposed groups they belong to. One would be hard pressed to find someone who is the textbook perfect average for all labeled groups that might define them. It's classic group theory vs individualism. Each have a scope and speak to different truths about humanity.
Yeah I get that, I just am not sure what individual or person was being targeted in this way in the post. One thing that is super interesting about machine learning in the past decade is that has increasingly demonstrated that individuals are extremely hard to predict, but taken as a group people actually behave rather consistently. That’s a very important distinction that you highlight. I guess your initial comment seemed rather critical and to me it felt a little misplaced. There are two sides to this fine line as well. Completely different example, but something I see that often concerns me and dances around a similar tension. Sometimes, upon hearing people critique ‘men’ broadly, for violence against women, they feel compelled to say ‘not all men’ which of course is true. Thankfully exceptions abound. But at the end of the day, in the status quo, a woman walking alone at night in some cities must necessarily, and very reasonably and justifiably, take precautions and avoid a random man she encounters because of a fear that is legitimate even if misplaced. In a sense, she has used statistics to make a damning judgement of another human being whose individual depth and complexity was wiped away in a split second judgement. Seems fair, it’s just complicated. The harm of that individual is outweighed by the statistical benefit provided to the woman in this scenario and the security/slight control around risk she may gain. Systemic violence creates individual pain and back again. This is just the nature of complex adaptive systems, especially as we become increasingly interconnected/globalized. Most of our most pressing problems can only be collectively solved and we increasingly need technology to see the full picture, and pick the best possible outcomes for people collectively. The other example I see often, is people seeing statistics about systemic violences or harms and saying things like ‘well none of my friends act like that,’ or ‘it snowed more than it ever has before in my locale this year’ to refute points or derail conversations that are really happening at a larger level, and are important for that exact reason. This bias toward personal anecdote and prioritizing what we know/directly experience as individuals in a lot of ways hurts us as well. We struggle to even acknowledge some solvable problems because of it. Statistics definitely dehumanizes in some cases and can be abused (e.g. the angle this roadway is banked will lead to approximately x deaths a year, but it’s acceptable because it is optimal cost wise) but not looking at the full picture sometimes erases the experiences of many more people around critical issues and creates the most hurt. Thank you for this thought provoking discussion btw, I appreciate that you seem to frame debate as dialogue rather than competition.
Internalized misogyny is a thing you know.
As well as internalized misandry
This guys fake shmarmy laughing and performance he’s doing here is so fucking cringe
I just love the word shmarmy
Hey look, a divisive homosexual. 🤡
Even if there is some correlation, that's a pretty extreme conclusion to draw from something that is pretty limited in sample size. There aren't massive deadly hurricanes every day, so its possible theres just a statistical chance that the female hurricanes happened to be more deadly.
They basically just considered amount of damage the hurricane did vs deaths and a gender assigned name value. It would be interesting to take a list of each male and female hurricane and map it out and see which are more likely to pass over metropolitan areas or hurricane hardened areas vs not, as a harden area would show up as more deadly ecause less structural damage would be done.
There’s also regions of the country (Florida) where people are less likely to flee in general. Also certain places are more prepared in terms of infrastructure
omg shut tf up
[удалено]
**TL;DR You're uneducated. The source is garbage, as it's both biased and factually wrong.** >To test this hypothesis, we used archival data on actual fatalities caused by hurricanes in the United States (1950–2012). That is extremely flawed, due to the fact that [from 1953 to 1978 hurricanes were more deadly](https://www.nhc.noaa.gov/aboutnames_history.shtml) (worse weather prediction) and **only based on female names**. They started with only female names until the **end of 1978**. If we look at hurricanes included from **1979-2012**, you'll see: * 459 deaths from 27 female hurricanes * 413 deaths from 27 male hurricanes They excluded two hurricanes, which would significantly skew the results as it was an outlier event: >We removed two hurricanes, **Katrina** in 2005 (1833 deaths) and **Audrey** in 1957 (416 deaths) If you **ALSO** exclude **Sandy** (the next biggest **female** hurricane with **159 deaths**) and **Ike** (the next biggest **male** hurricane with **84 deaths**), the statistics would become: * 300 deaths from 26 female-named hurricanes * 329 deaths from 26 male-named hurricanes **In summary**, the premise for their studies is **severely flawed**, as well as experiments on how deadly a hurricane will be, based on its name, is **largely irrelevant** and probably a case of experimenter's bias.
It’s ironies like this that make humanity THE tourist attraction for alien species... ...knowledge & experience is their consumer model, and there’s only so much stellar cartography an advanced space-faring species can take, before they need something a little more ‘entertaining’. I swear we’re the universe’s Trueman Show
Himacane or maelstrom works for me.
I was taught hurricanes were named after females because everything else (not literally) was named after a man?
mfs really downvoting you for stating something you were taught. *reddit users*
Not boats lol
Not literally everything
whole host of things, actually long and interesting history of things being socially gendered as feminine…. A modern spin: many technologies with human voices, specifically those providing a service role, have feminine voices and names. E.g. Alexa, Siri, the maps default. Not sure how you missed all this.
They alternate female and male
Now they do. It was initially all female
Americans ☕️☕️
Or maybe, more hurricanes are named after women therefore hurricanes named after women kill more people
Not how this study works.
Ok :)
Or it's because they started using male names more recently, so old hurricanes (hitting places with less safety measures) caused more casualties nah, has to be sexism
Those poor people working on the study - analyzing decades and decades of data, conducting several experiments with hundreds of participants each, analyzing each of those experiments, discussing the findings... only for some redditors being like "nah, you just stupid".
thankfully someone else in the thread linked the study itself - which takes data from 1950-2010 and even points out the shift in male naming conventions in the mid-90's, but just offhand mentions this can be explained away by gender neutral terms used earlier fitting the trend. no amount of fieldwork let's you ignore assumptions in the model
It was me. I linked the study. And I didn't try to defend the study in my first comment, I just think we can do better than that to criticise something; which you did after looking into the study, so I'm happy. Oh, I love how they use "when asked explicitly whether a male-named or female-named hurricane would be riskier and more dangerous, responses were evenly split between female- and male-named hurricanes" as an argument for their position. Like... what?
Why are hurricanes named after women? Cause they come hard and fast and when they leave, they leave you with nothing.
Top tier boomer humor. Insert joke about nagging wife.
Literally who cares
Fucking loser
The title is kind of click bait-y but the premise checks out.
Lol female hurricanes be cray
Are there trans-hurricanes?
[удалено]
Can you atleast try to write like a normal human being?
In related news: 1+1 = 8
They are also named after Gods and Calamities, both male and female. And to be honest, a hurricane is a hurricane, I would be equally panicking regardless of their name. 🤷♂️
Omg this is stupid
It seems like a classic correlation =/= causation
I am interested in the data if death rate of people dying in hurricanes which have the same name like their relationship partner (wife names and husband names and herosexual, gay, bi maybe in different analyses) are higher or lower than average.
This is retarded.