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Also, Also, please tell me how I have "crossed the line". And most importantly, again, please point me in the direction of a journal peer-reviewed paper about the analysis of trends in ground temperature in Australia that one can replicate. Please, please do this. It would be crazy after all, if Australia was making all sorts of policy if there hasn't even been a paper prsented in a journal about Australian temperature analysis. Surely.

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ohh yes sorry, I almost forgot, please tell me how I have made up, manipulated and concoxed the results. Thanks.

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JLowe asks:
"please tell me how I have made up, manipulated and concoxed the results"

You know perfectly well that having not submitted your work to peer review this is impossible. Your request is disingenuous.

Now with respect to the reality of global warming in Australia please feel free to explain why it is that you are right and the following is not.

"Australia is one of the many global regions experiencing significant climate change as a result of global emissions of greenhouse gases (GHGs) from human activities. The average surface air temperature of Australia increased by 0.7 ?C over the past century ? warming that has been accompanied by marked declines in regional precipitation, particularly along the east and west coasts of the continent. These seemingly small changes have already had widespread consequences for Australia. Unfortunately, even if all GHG emissions ceased today, the Earth would still be committed to an additional warming of 0.2?1.0 ?C by the end of the century."
http://www.csiro.au/csiro/content/file/pfbg,,.html

"The CSIRO says the drought can mostly be attributed to Australia's normal weather patterns but says global warming has intensified it."
http://www.abc.net.au/news/newsitems/200610/s1765929.htm

The problem here Jonathan is that you are standing up in a public forum and claiming that CSIRO is wrong. You are proclaiming that a 40 page consultancy report written for the Australian Business Roundtable on Climate Change by CSIRO's Drs Benjamin Preston and Roger Jones addressing the impact of climate change on Australia is wrong.

You provide not a single byte of data to give anyone any reason to believe that you, a doctoral candidate, who has yet to submit to peer review, who has yet to explain (in scientific terms) why the most prestigious science organizations on the planet are wrong, who has yet to explain how people who have earned their PhD's are wrong, are essentially saying "Trust me." Not going to happen. Not even at scienceagogo.com.


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Count Iblis II, I'm just saying that Australia and parts of Antarctica have not increased in temperature of the ast 150 years. Period. Nothing else. Whilst it makes sence that Co2 would increase temperatures like it does in a greenhouse, it obviously is making no significant effect on Australia and parts of Antarctica. Let me say once again. There is no evidence to say that Australia and parts of Antarctica are warming up.
That's a very unscientific statement if you don't tell what "significant" means. "Not significant", as you define it, is that any change falls within your confidence limit. But if you are obscure about your confidence limits and just say "not significant" than that's a meaningless statement.

Your methods are only useful where a small effect would not be a problem. E.g. if you test the effectiveness of a new drug using a double blind trial, then if the trial turns out to be negative, you won't be interested in that drug, even if it does have a small effect that was not detected because it falls within the confidence limits.

In the case of Global Warming, the observed effect falls within your confidence limits, so you results are irrelevant.

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Well if we want to quote media outlets as proof then go ahead, I could give plenty in return. But considering that you can not mention one peer-reviewed paper on australian temperatures, I will list one for you:

Karoly, D.J., and Braganza, Attribution of recent temperature changes in the Australia region.

which only looks at maximum and minimum temperatures. It is rather a weak statistical analysis. Their conclusion that minimum temperatures has increased agrees with my findings, but when we keep the time variable constant, as we should, we find no increase in temperature, and a slight increase (tho not statistically significant) at around the 3pm mark - heat of the day.

Morgran, I suggest that we conclude here that we agree to disagree, and stop the comments. And I will agree to to forget that you have not told me how I apparently made up and manipulated data and have crossed the line. Because this is getting no-where and is not worth the time.

I will however look forward to your comments on my paper regarding Australian temperatures which will be released along with my PhD in the future.

Thankyou and best of luck.

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My reply to Jonathan (on his blog)

"The p value represents the probability that the result is due to chance or natural variation."

No it doesn't. It is the other way around. It represents the probability that, in a hypothetical setting where reality is described by some so-called "null model" according to which there is no trend, you would see a deviation stronger than you've observed.

That deviaton pointing to a trend would then have to be due to pure chance alone. So, the stronger the deviation from the null model, the lower the probability.


This sort of simplisic tests are not the way to detect small subtle effects when you have limited data. You can always assume some null model and then say that you didn't detect a significant deviation from the null model.

If you want to translate your p value to the probability that there is a trend, then you need to know how likely a trend is a priori. This is difficult to estimate. However, this does tell you that it is unfair to take your null model to be something that is regarded to be a priory unlikely.

So, perhaps you should present your results differently. If you take the observed global warming for the Earth as your null model, then how significant is the deviation you have observed?

Or put differently, what is the probability that if Australia is warming as fast as the rest of the Earth, then how unlikely would it be to observe a deviation as large or larger than you've observed?

If that's lower than 0.05, then you do have an important result.

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I'm sorry Count Iblis, here's my reply:

Umm, Count Iblis, you are wrong. It's the other way around. A p value less than 0.05 represents a deviation from the null hypothesis. And it's got nothing to do with a hypothetical reality.

eg a p value of 0.03 proves a significant difference, there is only a 3% chance that the difference is due to chance, and a 97% chance that there is a significant difference. A p value of 0.7 suggests a 70% chance that any deviation is due to chance, and a 30% prob that it isn't. Other way around mate.

"This sort of simplisic tests are not the way to detect small subtle effects when you have limited data."

They are the only way

"You can always assume some null model and then say that you didn't detect a significant deviation from the null model."

Nope wrong there. We are testing if the difference in temperature we may/may not be seeing now is due to natural variation or not. Significance tests is the only way to test this.

"You can always assume some null model and then say that you didn't detect a significant deviation from the null model."

Umm, this is completly the false way to do scientific tests. The null is always that there is no differnce, the alternate is that there is.

I am afraid you are wrong on all counts here Count Iblis, please feel free to read a 1st year statistics book.

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Utter nonsense Jonathan. This just shows that you don't understand much about the complexity of the problem and therefore your simplistic methods are inappropriate.

I find it shocking that a Ph.D candidate doesn't know the basics of probability theory, in this case Bayes's theorem relating the probability of finding a certain data set given some scenario X and the probability of some scenario X given the data set.


I guess that you've read dumbed down statistic books. In high school we were taught this subject also, but even there we were taught the subtleties of this.

"The null is always that there is no difference"

Well, why not assume a null according to which there is no difference in the incidence of lung cancer and smoking in Angola. No such tests have been done there. Do a small test such that any reasonable effect will fall within your confidence limits. The result will point out that there is no significant increase in the incidence of lung cancer compared to non smokers in Angola.

Now you could dismiss this example as a badly chosen caricature of your work. But let's just take this example as an illustration that you canot reverse the probabilities. The probability that the data is significant according to the null model and the probability that the null model is true are not, in general, simply related.

A null hypothesis that there is no link between lung cancer and smoking in Angola is unreasonable given all the data from the rest of the world. So, one has to do the study the other way around to see if the link betwen lung cancer and smoking is significantly different in Angola than inthe rest of the world.

I suggest you do the same for climate change in Australia. I'm not saying that your data is wrong, or that you've manipulated data. You've just calculated something that is of no interest.

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wow wow wow, Count Iblis II, calm down dude. Are we talking about Bayes's theorem or not? Sorry dude, completly different matters.

Please tell me what your credentials are in statistics. Please do. I have a Bsc (hons) and MSc in statistics. According to wikipedia, the root of all correctness (lol!) the p value is "the probability of obtaining a result at least as "impressive" as that obtained, assuming the truth of the null hypothesis that the finding was the result of chance alone."

So you are completly wrong.

"I guess that you've read dumbed down statistic books. In high school..."

LOL LOL LOL LOL LOL LOL LOL LOL LOL. You are saying that your high school knowledge of how statistics works is better than my 9 years at university? please. I encourage you to read a first year university book of statistics before you so make a fool of yourself once again. Sorry, but you really dont want to be making big stuff ups like this again when trying to prove me wrong.

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Credentials are irrelevant, Mr. Nobody can publish in Nature if he has the results that merit publication. But anyway, I'm an expert in statistical physics and qauntum field theory. I know a lot about probability theory, but I admit that I don't have a lot of hands on experience with handling huge amounts of data. I've just completed my Ph.D. and I've published 15 articles in peer reviewed journals.

"So you are completly wrong."

No, because credentials are irrelevant, only the results and argumentations count. Prof. Dr. X can be wrong and Mr. Nobody can be right. This is how science works.


"the probability of obtaining a result at least as "impressive" as that obtained, assuming the truth of the null hypothesis that the finding was the result of chance alone."

That's what I said all along (you assume the null model). In this case, all you've said is that probabilities are not as low as required to rule out a model. But that model is not the favored model that climate scientists assume. Your results would be much more interesting if you turn it around and show that Australia's temperature trend is significantly below that of the global trend. Because then you rule out the standard climate scenario with 95% (or higher) probability, assuming what the climate scientists say is happening for the rest of the world.

That would be a very important result that you can perhaps publish in Nature. Otherwise you have a (literally) insignificant result. You usually cannot conlude very much from the fact that some probability is, say, 0.3 and not as low as 0.05. assuming some model. It is neither strong confirmation of that model (because you started out assuming that model to be true), nor is it evidence that that model is wrong.

It shouldn't be so difficult to subtract from the data you've used the global trend and see if there is a significant downward trend. That could well yield a significant result, in which case I look forward to reading your publication.

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Actually, subtracting the global trend is not completely trivial, because there are error bars on that too, but it is still pretty standard stuff in data analysis

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JLowe wrote:
"Well if we want to quote media outlets as proof then go ahead"

CSIRO is not a media outlet.

As a doctoral candidate it would seem to me you could do better than to:

1. Proclaim your PhD before it has been awarded.

2. Not step up and acknowledge that you are trying to defame CSIRO by claiming they are wrong.

I wonder if the reason you are posting in blobs and at SAGG isn't related to the fact that your dissertation was rejected: I wonder.


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Are we talking about Bayes's theorem or not? Sorry dude, completly different matters.
No, it's not. You ignore this when you turn your results around and make statements like:

Quote:
Australia hasn't even warmed up: stats prove it.
This statement suggests that given your data, the probability that Australia is heating up as fast as the rest of the world is very low. But you never calculated that probability.

Suppose that if temperatures increase according to some rate r the probability (in the followiung probablity = probability density where appropriate) that you measure some data set D is:

P(D|r)

Your results are about the function

P(D|0)

I.e. the probability that you observe data D given that the rate r is zero. Now, no one cares about this function! What we want to know is the probability as a function of the rate r. I.e. what is:

P(r|D)

This is the probability that the rate is r, given that you have observed data set D in your experiment. How do we compute one from the other? In general you can reason as follows:

P(x)*P(y|x)=P(x,y) (1)

Here P(x) is the a priori probability of x, i.e. the proability that x has a given value before you do any measurements, P(y|x) is the probability that you find variable y (say your observed data set) given x, and P(x,y) is the joint probability that you find both x and y at their respective values. This joint probability is, of course, symmetric in x and y, so you can also write:

P(x,y) = P(y)*P(x|y) (2)

From (1) and (2) you find:

P(x|y) = P(x)*P(y|x)/P(y) (3)

P(y), the a priori probability of y, can be written as:

P(y)= Integral over x of P(x,y) dx =

Integral over x of P(x)*P(y|x) dx

So, we find:

P(x|y) = P(x)*P(y|x)/[Integral over x of
P(x)*P(y|x) dx]

If we take x to be the rate r and y the data set D:

P(r|D) = P(r)*P(D|r)/[Integral over r of
P(r)*P(D|r) dr]

Hypothesis testing like you have done is basically putting high odds on the null hypothesis, in your case this amounts to assuming that P(r) is strongly peaked around r = 0. Then, for P(r|D) to shift away from r = 0 you need data for which P(D,0) is very low compared to P(D,r) for some larger r.

In general we don't know what P(r) is. However, we can reason as follows. If, under the assumption that P(r), Bayes's formula implies that P(r|D) is not at all strongly peaked around r = 0 then that is strong evidence that it isn't strongly peaked around zero. If we had made a more reasonable assumption about P(r), i.e. starting out with a less sharply peaked distribution about r = 0, then P(r,D) would have shifted even further away from a sharply peaked distribution about r = 0. So, we certainly cannot be accused of having "planted" the result we found.

But if the data does not lead to a P(r|D) that extends to signifiantly larger r when you assume a sharply peaked P(r) about zero, you cannot conclude that you have found proof that P(r|D) is indeed sharply peaked around zero, simply because you started out with that assumption. Here you do put in the result you find back.

Observations of climate change suggest that r is 0.6?C +/- 0.2 ?C per century. If you say that your data for Australia would compell one to believe that this is not the case for Australia, you have to show that if you start out with P(r) peaked around 0.6 and a standard deviation of 0.1 the function P(r|D) becomes peaked around much lower values for r, such that the probability that r is in the range 0.6 +/- 0.2 is pretty low.

You have done nothing of the sort. I'm not sying that your data would not yield such a strong result if you analyze it this way. All I'm saying is that you haven't used your data to prove anything other than you already put in from the start. In science, that isn't considered to be a strong result, especially if there are other results that suggest otherwise and if you want to dispute those results.

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Morgan, another unwarrented unproven accusation. I'm over it. Count Ibis, I have never said or proven anything about the rest of the world, only that Australia is not heating up. That is all.

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Quote:
Originally posted by JonathanLowe:
Morgan, another unwarrented unproven accusation. I'm over it. Count Ibis, I have never said or proven anything about the rest of the world, only that Australia is not heating up. That is all.
It would be better to say that you have failed to rule out the null model according to which Australia is not heating up. But this can be due to the fact that Australia is indeed not heating up or because of the limited dataset.

Try to use your data to get some significant result either way. If your data shows that some model (be it a null model accoding to which Australia is not heating, or a model that assumes the global temperature thend for Australia of 0.6?C per century) is outside the 95% confidence interval, then you have a significant result that is publishable.

What you want is a strong result about the rate at which temperatures are increasing in Australia. If all you have is that you didn't see a significant result then that can mean many things. If your data set isn't very large it usually means that you cannot rule out either scenario. If you can't do that then you can't claim that Australia is not heating up as fast as the rest of the world.

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Well said Count.

And since we know nothing of the data set Jonathan Lowe claims to have purchased, or what he did with that data to achieve his "published" result what is null and void is any ability to draw a reasoned conclusion.

What is most telling, however, is that CSIRO, NOAA, and NASA have been able to draw conclusions based on the data available. And that conclusion has universally been one of climate change.

If someone is going to claim Einstein wrong they need to deliver the goods. If someone is going to claim CSIRO wrong the same holds true. The burden of proof is always on the prosecution ... not the defense.


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Please see here for the analysis: http://gustofhotair.blogspot.com/2006/11/analysis.html

Unfortunetly all research into Australia's temperatures have solely looked at maximum and minimum temperatures only. This, it would seem, is not the best way of looking at it because it allows one variable, time, to vary. By keeping it constant we can get a better view of where, and when australia is apparently heating up.

This will most likely be my last message on this thread. Thanks for the input and attempts to teach me statistics and reidicule me. I am looking forward to my PhD, and then maybe when my paper comes out about australian temperatures, you are most welcome to provide a critical analysis of it. Until then

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I am confused. Why would anyone with Bachelors, Masters, and working on a Doctorate point anyone to something titled:
http://gustofhotair.blogspot.com
when referring to serious science? The incongruity is staggering.

But in spite of that I did actually read the blog. And what I read is, to be quite frank, impossible to take seriously. You have not done the one thing that science students are taught is always required. You have drawn a conclusion at odds with prior art and have not, or more likely can not, explain why that prior work is incorrect. That flaw alone is fatal.

You wrote:
"Data is taken from the Australian Bureau of Meteorology (ABM)"

What data? The above statement is meaningless. Impressive, perhaps, to the layperson but meaningless.

"From these averages we calculated the deviations from the mean for every month of every year for each station."
but you also write:
"These were then summed to get the average deviation from the mean for every station for every year."

Do you really think these statements have meaning?
Would any statistician accept either of these two much less be able to decipher the obvious discrepancy? Worst yet you don't even use common statistical terms properly.

And, if you cleared up the obvious contradictions, why is this methodology valid? Is this the methodology used in papers published in peer reviewed journals and accepted by the community of climatologists?

A rhetorical question because we both know it is not. What you did rises to the level of a high school science project not a PhD thesis.

JLowe wrote:
"Unfortunetly all research into Australia's temperatures have solely looked at maximum and minimum temperatures only."

If you really believe that the PhD's who have published peer reviewed papers are that pathetically incapable of doing their job properly you are soon to have a rude awakening.

BTW: Do you remember posting November 15, 2006 10:32 AM at this site as part of this thread the following which I quote:
"thru my PhD in statistical analysis of climate science"
and now you write:
"I am looking forward to my PhD"
and at another site (http://www.scienceforums.net/showthread.php?t=23336)
you wrote:
"My first 2 years of my Phd were in Mathematical Statistics"

Apparently you don't even know whether you have a PhD or what it is in.

You have contradicted yourself as to your methodology. You have contradicted yourself with respect to your academic accomplishments. You have misstated a basic concept in statistics for which you were corrected by Count Iblis, and your usage of statistical terms (above) is hopelessly elementary.

I am left to seriously question whether anything you have written is true. And I really don't mean to drag this out but:
You claim 9 years of college but never name it ... Why? What college?


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Morgran, damn. I had hoped this was over, but I have to defend myself again instead of my work.

I have completed 2 years of my PhD in sports statistcial analysis, and stopped because my own work was increasing at such a rate, that I couldn't keep my PhD going. I am in the process of restarting my PhD in climate change analysis, from scratch. The quotes are not contradictory.

Previous research, like the one's I posted before have only looked at maximum and minimum Australian temperatures.

?I am confused. Why would anyone with Bachelors, Masters, and working on a Doctorate point anyone to something titled: http://gustofhotair.blogspot.com
when referring to serious science??

Why would anyone discuss science at a place called scieneagogo? Perhaps we should stop all scientific talk at forums and blogs and just allow discussion in peer-reviewed journals.

?You have drawn a conclusion at odds with prior art and have not, or more likely can not, explain why that prior work is incorrect?

Wrong. I have actually agreed with previous research that minimum temperatures and maximum temperatures have increased in Australia over the past 40 years. Over 100 years, the area of minimum temperature increase is significant, however the area of maximum temperature increase is insignificant. My work goes deeper than that and actually analyses temperature changes throughout the day.

?"From these averages we calculated the deviations from the mean for every month of every year for each station."
but you also write:
"These were then summed to get the average deviation from the mean for every station for every year."

Do you really think these statements have meaning?
Would any statistician accept either of these two much less be able to decipher the obvious discrepancy? Worst yet you don't even use common statistical terms properly.?

This is the method that the ABM use to calculate the mean annual Australian maximum and minimum temperatures as shown in their website.

??you are soon to have a rude awakening.?

Please tell me of a paper that looks at Australian temperatures at different times of the day, thus keeping the time variable constant. Is the maximum temperature occurring at different times today than in the past? What about different areas? Are we heating up at the heat of the day, or is there a constant increase in temperature throughout the day? How has Australia been heating up at say, 3am? What about in the different months/seasons/areas? How has Australia been going in temperature for days that have a large section of cloud cover as opposed to not? What about times the relationship between cloud cover and temperature at a certain season at say?..6pm? or midnight, or 3am? Is solar radiation a significant factor in increased temperatures when there is cloud cover during the heat of the day? Why has Australia in the last 5 years only increased in temperature when the sun is out?

I?d love you to point me to the supposed multitude of peer-reviewed journal papers that have already answered the above questions. We surely would know all the answers to these before we start spending billions of dollars. Would hate to spend so much money without knowing the full scale of it all.

?You have misstated a basic concept in statistics for which you were corrected by Count Iblis?

I was merely explaining the ?p-value? to a person who has never heard it before. I was not going into a full blown analysis of it.

?You claim 9 years of college but never name it ... Why? What college??

Monash University, Melbourne, Australia. Probably with the University of Melbourne, the two most highly ranked Australian universities: http://www.monash.edu.au/

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To be honest I stopped reading when I got to:
"I have completed 2 years of my PhD in sports statistcial analysis...."

I am sure you can understand why. But in case you are not ... it is because on November 15, 2006 10:32 AM you wrote: "thru my PhD in statistical analysis of climate science."

There is no way that these two statements can be reconciled and you know it.

There is no need to defend yourself as your position is indefensible. You seem to be acknowledging that you have (to be politically correct) intentionally misstated facts.

Short of advising you to issue an apology to your advisor at Monash I see nothing of this thread that is credible and suggest you ask Kate to remove it from SAGG to avoid further embarrassment to your school.

Perhaps you could start a new thread on the application of statistical analysis to sports. A topic on which you might be credible.


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