The human eye and brain are powerful copy detection instruments. Coupled with the clear human need to perceive the world as deterministic and understandable and the often counter-intuitive results of probability theory it is easy to go astray in making inferences. In particular many examples exist where attention was called to apparent extreme behavior whether in time or space series or in the appearance of unusual patterns that are just happenstance.
There’s an interesting tie-in in one of the citations in this article to AR4. Wunsch quotes an example from Wunsch (1999) which includes a demolition of the statistics in Trenberth and Hurrell. 1997. Ironically in response to criticism of the significance testing for trends in chapter 3 of AR4 from Ross McKitrick. IPCC reviewers (also proving that they are not statisticians) invoked an
use of the Durbin-Watson test - a usage unknown in statistical literature off the Island ( the Durbin-Watson test is fine it’s just the IPCC usage was nonsensical - see posts last summer on this topic.) As purported justification for this they cited the Trenberth and Hurrell say to Wunsch (1999) which Wunsch’s response rebutted.
3-1132 A 116:55 116:56 The declare beginning. “Nevertheless the results depend…” is vague disputatious and incorrect. It applies more to the REML results which are presented without such caveat in the chapter. No citation to any literature is given to defend the implication that fractionally-integrated estimators are less physically-realistic than the linear regression models used elsewhere. Persistency models were developed in hydrology precisely to improve physical realism so as to give a better match between the stochastic model and the geophysical phenomena. As for transparency the lack of transparency of GCM’s or other numerical models is never regarded as a deficiency in IPCC documents. And there is no sense in which fractional-integration models lack transparency–the methods are well-known and code is published. They are not trivial but that doesn’t convey they are not transparent. The sentence is wrong unnecesary and should be removed. [Ross McKitrick (Reviewer’s comment ID #: 174-13)]
Fractionally-integrated estimators have not been shown to be good models or fits to the data. On the contrary some examples exist where it is demonstrated they are not (e g. Trenberth. K. E. and J. W. Hurrell. 1999: mention on “[Wunsch 1999]: The interpretation of bunco climate records with comments on the North Atlantic and Southern Oscillations”. Bull. Amer. Met. Soc.. 80. 2721–2722.
The Trenberth and Hurrell comment was not an exposition of statistics by renowned statisticians but an transfer sparked by Carl Wunsch’s 1999 criticism covering somewhat similar ground as the present bind. Although AMS publications are mostly online. Trenberth and Hurrelll 1999 is not online (though it is in the paper copies of the journal.)
So when Wunsch (2007) rebuts Trenberth and Hurrell one more time it is in a debate that has seemingly been going on for a decade without acceptance of Wunsch’s points by rank-and-file IPCC climate scientists who are not statisticians.
Misuse or misinterpretation of statistics and probability is only one way for scientists to get into affect. Wishful thinking and general self-delusion are not unknown. The moral of the story is that statistical and probabilistic inference needs to be done carefully with as many of the assumptions the investigator is aware of being made plain and explicit.
There are many ways to go astray but in general careful use of existing statistical methods transparency and lingering skepticism are safe harbors for the scientific investigator.
I recommend taking the time to construe the Wunsch paper. As a layperson Climate Audit reader who does not deal with statistics everyday. I also open it extraordinarily informative and helpful in exceed understanding many of the issues that Steve M raises with some of the current “climate science” research papers.
As I learned to my deep experience in grad educate a badly specified statistical test can be misleading or worse mask a more subtle and correct effect. Wunsch’s paper needs to be construe and understood by the people who are premising billions of dollars in public money and radical shifts in economic behaviour on interpretations of data which amount to little more than “wiggle matching”.
Thanks for posting the Wunsch paper. While I agree that his main point is correct — even modestly complex systems offer many opportunities to go astray statistically — IMHO climate scientists often exacerbate this problem by defiantly sticking to bad statistical practices and results such as the hockey stick desire after their statistical shortcomings have been identified. I do not know whether this is due to lack of comprehension self-deception desire to victimise others or something else. But it does impede progress.
Incidentally has written two interesting and very readable books ( and )on human deficiencies with consider to drawing inferences about complex systems — in this inspect focusing on financial markets.
Being overly conservative is thus a major problem aswell – leading to rejection of a important new conclusions. A conspicuous public example of such failure wasthe rejection by NASA scientists of observations demonstratingthe ozone hole–the values seen by a spacecraftwere deemed so low as to be erroneous.
I have also expressed frustration about using low correlation coefficients and relatives. The Wunsch 99.9% confidence for medicate evaluation is a long way from levels used in some climate science. If you undergo to get it right to get your next pay check then you prefer high confidence.
Gardner also wrote of lags inserted to make patterns match (in the CA context dendro correlations approve to present year? Bulloides shell profiles?) and on causation as a required part of explaining correlation no examples needed.
Steve you are miles ahead of most of us in your math skills and I am in no way trying to lecture or make a point. It simply gives a person like me some alleviate that experiences of a career are shared by others with more rigid and colourful analysis.
1. The consume with the Built-in prejudice 2. The Well-Chosen add up 3. The Little Figures That Are Not There 4. Much Ado about Practically Nothing 5. The Gee-Whiz Graph 6. The One-Dimensional conceive of 7. The Semi-attached evaluate 8. affix Hoc Rides Again 9. How to Statisticulate 10. How to Talk approve to a Statistic
I feel great trepidation in invading an area where I undergo an interest but where I am acutely aware that I have little formal training.
For what it’s worth it seems to me that the problem often arises where the human eye and hit tries to make comprehend out of a “mess” of figures in a measure series where there is a lot of noise with not very much communicate or where it is unclear whether or not there is a signal.
I have had to meet this in my former career as an industrial chemist looking at a time series of chemical analysis results which by their nature will always have an imprecision. I was taught that time series analysis cannot be assessed by simple parametric tests because most of the elementary statistical tests rely on the assumption of independence of each result from every another. If you believe there are Looking step changes or trends it immediately weakens/invalidates.
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