Right. Well, Dr. Mann, whatever you are qualified to do, science is not part of it. You have just made the mistake of all mistakes. There are no "proprietary algorithms" in science. Newton owns no patent on the law of gravity. If you knew more about science, you might know that. As it is, you're a hack.
Now, just as a hint for you nontechnical readers: whenever you see a statistical series that has been "detrended" you should know two things: 1)most detrending methods will show a trend in data like a sine wave, which has no trend. 2)a good detrending algorithm is workable with SCALAR data: data with a meaningful zero point (in temperature, that would be degrees Kelvin: in that scale, a number twice as high is twice as warm. This is not true in degrees farenheit or celcius). A quick tutorial:
means that the number stands for something (e.g., red=1, blue =2). Nominal data are found by counting or enumerating variables. Nominal and ordinal variables are sometimes called category variables. Their data are often referred to as discrete. Finding the average of nominal data will seldom be meaningful.
means that the numbers indicate order (e.g., 1 = always 2= often). Like nominal data, ordinal variables are counted or enumerated. Nominal and ordinal variables are sometimes called category variables. Their data are often referred to as discrete. Some analyses will not be meaningful with ordinal data.
* Scale (interval or ratio)
refers to the differences between numbers. Scale data usually are obtained through measurement. Scale data are sometimes referred to as continuous. This type of numerical data is most useful in statistical analysis. The scale will have a zero in it which is objectively meaningful.
(thanks to SPSS for this quick tutorial.)
The hockey stick is vulnerable not just because of the question of data, but because of the fact that random data can be used to make the hockey stick appear if a similar method is used. Randomness means unpredictability: and the hockey stick is an illusion of knowledge. We have more work to do before we make pronouncements, and more analysis of the assumptions behind the methods of analysis.
Dr. Mann's contributions will be welcome when he decides to join the scientific enterprise and publish his data and methods, iinviting a critique of either. Until that time, he is engaged in finding little green men, and ought to be treated with the care we extend to all mentally impaired individuals.
Assignment for the day: "Aliens Cause Global Warming." Lecture by Michael Crichton at Caltech in 2003.
Questions for discussion:
1. What does he mean when he says, "This is not the way science is done, it is the way products are sold."?
2. Finally, discuss the difference between science, marketing, and politics in this quote:
I regard consensus science as an extremely pernicious development that ought to be stopped cold in its tracks. Historically, the claim of consensus has been the first refuge of scoundrels; it is a way to avoid debate by claiming that the matter is already settled. Whenever you hear the consensus of scientists agrees on something or other, reach for your wallet, because you're being had.
Let's be clear: the work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus.
There is no such thing as consensus science. If it's consensus, it isn't science. If it's science, it isn't consensus. Period.