"The calculations put chance of the piracy having occured due to natural variability at 0.5%. “So it’s 99.5% that it occurred due to warming over the industrial era,” said Best."
We need to train children in school to understand what statistical confidence means, so that we stop saying wrong things like this.
You can't say it's 99.5% due to global warming alone though. You can say 0.5% occurring by random chance but you can't just ascribe everything to one causal factor. Could be multi-factorial.
While the exact language in the quote is perhaps implying something, it only directly says the that the cause is warming during the industrial era. It doesn't provide any direct information about causal factors.
In the article: "So it’s 99.5% that it occurred due to warming over the industrial era," said Best.
I don't have a problem with them saying for example - "We believe that the river changed due to warming over the industrial era"
I don't really have a problem with anything they conclude to be honest, I was just answering another guy's question. Shitty of HN users to give me negative points over it. I feel like I'm on Reddit again.
That is possible. However in the abstract the author states
"Based on satellite image analysis and a signal-to-noise ratio as a metric of glacier retreat, we conclude
that this instance of river piracy was due to post-industrial climate change."
I played a game of Monopoly where I started with a roll of 12. There was a 97% chance I correctly rejected the hypothesis that the dice were fair. Maybe this is true in some sense? But it's still somewhere between misleading and nonsensical to say.
I think a better analogy is that it's like having many pairs of dice, and rolling each pair in turn until you get a roll of 12. Then concluding "this particular pair of dice must be loaded".
Presumably, the researchers did not select rivers at random to study, they selected this river in particular because of the changes it is undergoing.
Yes, I agree that p-value tests have flaws. If you look at the data to determine your hypothesis it's easy to overfit.
Bayes factor appear to solve this issue. I disagree that this is a basic education issue. It is a lack of agreement among scientists as to what statistical analysis is appropriate.
"So it’s 99.5% that it occurred due to warming over the industrial era"
There are at least a couple of statistical fallacies in this conclusion. And there isn't a lack of agreement about that.
One problem with p-value tests is precisely that people misunderstand what the p-value means, which is where basic education comes in. It could save people from believing a lot of things they shouldn't. (Like many health and fitness crazes over the last generation, for instance) Or at the very least, we could train science journalists.
I think you're mainly correct. The main issue is that there's an implied "given that the model from http://www.nature.com/ngeo/journal/v10/n2/full/ngeo2863.html is 'perfect' (at least for this case)". Since it seems unlikely that the model is perfect, the numbers they give are almost certainly inflated.
...as best I can tell, you have still swapped them? It sounds like you're still talking about P(null hypothesis|evidence), whereas p-values are about P(evidence|null hypothesis). (Well, not quite the latter but something like it.)
If you look at 200 rivers, it would not be surprising to find something that naturally occurs 0.5% of the time. It is not correct to say that there is a 99.5% chance that this is due to non-natural causes.
The wording is ambiguous. In general, trying to determine whether a statement about probability is correct or not requires more information than can be encapsulated in a single sentence. The English language does not help in this.
We're not going to get more information here without someone looking into where the .5% number comes from.
The method of Roe et al. is summarized as follows. Let 1L be the change in
glacier length over the past 130 years (∼1.9 km), and let σL be the standard
deviation of glacier length due to stochastic fluctuations in mass balance, b, from
natural, interannual climate variability. The signal-to-noise ratio is defined by
sL =1L/σL
. Likewise, sb =1b/σb
. Ref. 12 demonstrates that the two are related via
sL =γ sb
, where γ is an amplification factor that depends only on the duration of
the trend and the glacier response time, τ . The probability density function (PDF)
for sb
is generated by combining the signal-to-noise ratios of the observed
melt-season temperature and annual-mean precipitation trends, normalized by the
summer (bs) and winter (bw ) mass-balance variability (Supplementary Fig. 1a,b,c),
respectively. We take σbw = 0.3 myr−1
and σbs =0.5 myr−1
, based on the observed
mass-balance variability at Gulkana Glacier and the analysis of the global datasets
of glacier mass balance33. The glacier response time is given by τ =−H/bt
, where
H is a characteristic glacier thickness, and bt
is the (negative) net mass balance at
the terminus. We set H=590 m, based on the scaling relationship for glacier
geometry suggested by Haeberli and Hoelzle34 and measured cross-sections35; and
we set bt =−7 myr−1
, estimated by extrapolating the vertical mass-balance profiles
calculated by Flowers et al.8
, thus giving a central estimate for τ of ∼80 years. A
PDF is estimated assuming τ follows a gamma distribution incorporating a broad
uncertainty of στ =τ /4 (Supplementary Fig. 1d). The PDFs for γ and sb are
combined to give a PDF for σL
from the relation σL =γ 1L|
obssb
. This, in turn, is
used to evaluate the null hypothesis that 1L|
obs occurred due to natural variability.
Supplementary Fig. 1e shows our estimate that there is only a 0.5% chance
that the observed retreat of Kaskawulsh Glacier happened in the absence of a
climate trend.
The 0.5% quote is fine. It's turning it into a 99.5% that is not fine.
It's essentially like saying that a pair of dice that rolled a 12 has a 97% chance of being loaded, because there's only a 1 in 36 chance of rolling that high.
Let's assume that when a river gets redirected, a scientist goes and investigates it (which seems reasonable), and is asked what is the chance that it happened by natural causes, or is due to global warming. Based on the analysis that @acover shared with us, we can see that out of a thousand such dispatches, 995 will be due to global warming and 5 will be due to natural variability. So it seems correct for that scientist to conclude "It's 99.5% due to global warming.". In fact, saying that it's .5% due to natural variability is equivalent.
You are probably thinking of the jelly bean situation, where a test is performed to detect something within a sample population. The test has a failure rate, and the population has a base rate of occurrence.
In this case (thanks @acover), they are directly calculating the base rate.
Now, you could also ask "How many times in a year will a scientist be dispatched and be wrong", which is a again a different question (and is closer to the scenario you described).
I am out of my depths but I do not think you are correct.
The paper appears to create a model for a single glacier's retreat. Selects the parameters of this model by analyzing all glaciers. Then determines the retreat of this glacier is unlikely under this model.
The issue is that there is a sampling bias. This glacier is not like most glaciers [I assume]. It was not randomly sampled from all glaciers. It was selected because of a river diversion due to glacial retreat.
What did they do to compensate for this sampling bias? I don't see it.
>Let's assume that when a river gets redirected, a scientist goes and investigates it
This is a faulty assumption and is what leads to the wrong conclusion. The probability of 0.5% is for a randomly selected river. That is, if you went and examined 200 randomly selected rivers, 1 of them (on average) would be redirected due to natural variability.
That does not imply that the remaining 199 were redirected due to global warming. It does not even imply that the remaining 199 were redirected at all!
What is needed is the percentage of rivers that have undergone this redirection. Here's a simplified example: If it's ~0.5%, you conclude it's just natural variation. If it's >0.5%, you conclude that something (possibly global warming) is increasing the number of rivers that are being redirected. If it's <0.5%, you conclude that something is decreasing the number of rivers that are being redirected.
"shows our estimate that there is only a 0.5% chance that the observed retreat of Kaskawulsh Glacier happened in the absence of a climate trend"
The 0.5% has nothing to do with the river. It is their confidence that the retreat of the glacier could occur in the absence of a climate trend based on their model.
Can you clarify why it is incorrect to reverse that into the statement "We estimate that there is a 99.5 percent chance that the observed retreat did not happen in the absence of a climate trend."? I confess to a fair amount of confusion at this point :) I'm sure there is something subtle (or perhaps obvious, and my brain is failing) that I'm missing.
Oh I thought we were having an interesting discussion about the linguistic mapping between probability and regular English. Sorry for wasting your time. :(
> The probability of 0.5% is for a randomly selected river
Sorry, what is the probability .5% for exactly? The probability a river is redirected under global warming conditions? I didn't think that's what they computed. If it is, then my bad. :) I thought they had computed given that the river was redirected, what is the likelihood it happened due to global warming. Ah well, like I said, the details are the hard part. :)
Presumably "Machine Language" which is what we called Assembly, back when the translation between what you wrote and what the CPU actually did was pretty transparent.
Considering it was a Tor site, it's not like a user who didn't know what they were doing could end up there on accident. Also I'm not sure users means "only browsed the website" (could mean they had a profile, etc.).
Banning production I understand fully. But viewing, under the argument it promotes it? The TOR developers have done far more to promote it than any single viewer, especially if we consider those who never paid any money and use ad blockers. Would we say the TOR developers should face some sort of punishment for not working with governments to develop a version that works to stop this (such as integrating something which causes it to drop off the TOR network as soon as it detects an illegal file, probably biases the algorithm against false positives)?
At the very least, I think they should be using all the resources to go after producers and those paying for it.
Lots of people visit links on the clear web showing illegal and horrific acts. If the full extent of a crime is filling out an http form with a fake email to see some pictures and video, it's still not clear that this is so far beyond the pale that years of prison for hundreds of thousands of people is the best solution.
The problem is fundamental and serious, what to do when a growing number of people have literally nothing valuable to contribute to an economy. But horses aren't a good analogy to shed light on it.
Horses are more analogous to steam engines than people, in their historical function on the economy.
Horses are a bad analogy if you want to prove that AI wont take over most jobs.
They are however a great analogy if you want to see how technology once it reaches a certain point renders biological beings sub-par useless as part wealth creation.
> you cannot pick this up in any meaningful way in a "few months" of after hours/weekend study
You can't pick up coding like this either. See Peter Norvig's famous "Teach yourself programming in 10 years" article. The delta in the wisdom you obtain, between a few side projects over months and battle hardened experience with real products and code bases over years, is immense.
Yet the glycemic index of whole wheat bread is higher than Coca Cola. (Look it up!) I.e. the same amount of calories of bread will spike your blood sugar more quickly than Coke.
Metabolically, starchy foods including grains are a lot more like sugar than people who think they are making rational health decisions want to believe.
Other cultures manage to stay much healthier while maintaining bread and white rice as major parts of their diet, though. The biggest common differences, from what I can tell, are:
1) fewer total calories,
2) way, way, way less candy (to include soda, many breakfast cereals) and dessert food (milk shake with those fries? Dessert. Donut? Dessert. Coffee cake? Dessert.) on average,
3) fewer convenience foods, and relatedly, less added sugar in diet,
4) less deep frying
I'm less confident about these, but would guess dairy and fruit juice consumption also tends to be way lower in healthier societies. More cream sauces, yoghurt, and cheese, but much less drinking milk on its own, milk over breakfast cereal, grande Frappuccinos. Fruit juices more likely to be real and unadulterated, pricier, and less often consumed.
Not cutting out carbs period, or even limiting them to a tiny percentage of calories. This makes me suspicious of painting too much of the carb kingdom as problematic per se.
[EDIT] Formatting, "desert" to "dessert" because apparently I failed kindergarten.
Now consider that in human history, about 40% of men were able to breed. So 30% of the population goes extinct every generation without passing on genes, assuming all women reproduce. So with a few hundred iterations of this kind of 70:30 split, you could see how evolution could happen quite quickly even under normal circumstances.
We need to train children in school to understand what statistical confidence means, so that we stop saying wrong things like this.