By William M. Briggs, professional statistician
“J’accuse! A statistician may prove anything with his nefarious methods. He may even say a negative number is positive! You cannot trust anything he says.”
Sigh. Unfortunately, this oft-hurled charge is all too true. I and my fellow statisticians must bear its sad burden, knowing it is caused by our more zealous brethren (and sisthren). But, you know, it really isn’t their fault, for they are victims of loving not wisely but too well their own creations.
First, a fact. It is true that, based on the observed satellite data, average global temperatures since about 1998 have not continued the rough year-by-year increase that had been noticed in the decade or so before that date. The temperatures since about 1998 have increased in some years, but more often have they decreased. For example, last year was cooler than the year before last. These statements, barring unknown errors in the measurement of that data, are taken as true by everybody, even statisticians.
Th AP gave this data—concealing its source—to “several independent statisticians” who said they “found no true temperature declines over time” (link)
How can this be? Why would a statistician say that the observed cooling is not “scientifically legitimate”; and why would another state that noticing the cooling “is a case of ‘people coming at the data with preconceived notions’”?
Are these statisticians, since they are concluding the opposite of what has been observed, insane? This is impossible: statisticians are highly lucid individuals, its male members exceedingly handsome and charming. Perhaps they are rabid environmentalists who care nothing for truth? No, because none of them knew the source of the data they were analyzing. What can account for this preposterous situation!
Love. The keen pleasures of their own handiwork. That is, the adoration of lovingly crafted models.
Let me teach you to be a classical statistician. Go to your favorite climate site and download a time series picture of the satellite-derived temperature (so that we have no complications from mixing of different data sources); any will do. Here’s one from our pal Anthony Watts.
Now fetch a ruler—a straight edge—preferably one with which you have an emotional attachment. Perhaps the one your daughter used in kindergarten. The only proviso is that you must love the ruler.
Place the ruler on the temperature plot and orient it along the data so that it most pleases your eye. Grab a pencil and draw a line along its edge. Then, if you can, erase all the original temperature points so that all you are left with is the line you drew.
If a reporter calls and asks if the temperature was warmer or colder last year, do not use the original data, which of course you cannot since you erased it, but use instead your line. According to that very objective line the temperature has obviously increased. Insist on the scientificity of that line—say that according to its sophisticated inner-methodology, the pronouncement must be that the temperature has gone up! Even though, in fact, it has gone down.
Don’t laugh yet, dear ones. That analogy is too close to the truth. The only twist is that statisticians don’t use a ruler to draw their lines—some use a hockey stick. Just kidding! (Now you can laugh.) Instead, they use the mathematical equivalent of rulers and other flexible lines.
Your ruler is a model Statisticians are taught—their entire training stresses—that data isn’t data until it is modeled. Those temperatures don’t attain significance until a model can be laid over the top of them. Further, it is our credo to, in the end, ignore the data and talk solely of the model and its properties. We love models!
All this would be OK, except for one fact that is always forgotten. For any set of data, there are always an infinite number of possible models. Which is the correct one? Which indeed!
Many of these models will say the temperature has gone down, just as others will say that it has gone up. The AP statisticians used models most familiar to them; like “moving averages of about 10 years” (moving average is the most used method of replacing actual data with a model in time series); or “trend” models, which are distinct cousins to rulers.
Since we are free to choose from an infinite bag, all of our models are suspect and should not be trusted until they have proven their worth by skillfully predicting data that has not yet been seen. None of the models in the AP study have done so. Even stronger, since they said temperatures were higher when they were in fact lower, they must predict higher temperatures in the coming years, a forecast which few are making.
We are too comfortable with this old way of doing things. We really can prove anything we want with careful choice of models.
Well, George E. Smith – let’s test your “hypothesis” – A head in the oven and feet in ice water. This defines an integral or an average how? If you’re a mathematician, as you claim, why don’t you use descriptions which actually apply?
Now, by definition, an integral is a sum. An average is a sum multiplied by the inverse of the number of elements in the sum. So the difference between a sum and an average is a multiplier. We can’t get into calculus if you want, but no matter how you slice it my statement is exactly and unquestionably true.
Your image is colorful, but it’s fairly meaningless.
Your complaint is that the (min+max)/2 definition of average temperature is at best inadequate and at worst ludicrous. I coudn’t agree more. Your argument was still poorly made, your counter to criticism was equally so. You immediately assumed I disagree with you, and I don’t. What I was criticizing was what I saw and still see as poor arguments.
An integral is a sum. An average is a weighted sum. Talking about heads in ovens and feet in ice water doesn’t change that.
“”” Merrick (16:14:54) :
Well, George E. Smith – let’s test your “hypothesis” – A head in the oven and feet in ice water. This defines an integral or an average how? If you’re a mathematician, as you claim, why don’t you use descriptions which actually apply?
Now, by definition, an integral is a sum. An average is a sum multiplied by the inverse of the number of elements in the sum. So the difference between a sum and an average is a multiplier. We can’t get into calculus if you want, but no matter how you slice it my statement is exactly and unquestionably true.
Your image is colorful, but it’s fairly meaningless. “”
Well Merrick, I give up; you clearly didn’t read what I said; and since you didn’t read that and understand it, then you didn’t get the point of my “frairly meaningless” example.
To reiterate; I said that climatologists (in their writings) define climate as the long term average of weather. Note the two terms climate and weather. Nowhere is the word “temperature” mentioned. That is their definition not mine.
And I asserted that I believe that climate is the integral of weather. Once again no mention of the word temperature.
Weather and climate encompass a whole lot more variables than simply temperature. And the planet as a whole reacts to the continous sum of THE EFFECTS of all those variables that change during the course of what we call weather.
I’m sure there are some aspects of both weather and climate that might react to the average of temperature; but what about the many effects of both climate and weather that are a direct result in DIFFERENCES of temperature from place to place and from time to time.
It is the integral of THE EFFECTS of weather that makes its mark on the planet.
So if you believe that the integral of weather (not temperature) is just a factor times the average of weather (not temperature); what value do you presently have for that constant (or varying) factor that relates the integral to the average.
And to revisit my fairly meaningless example; I quite deliberately chose the extremes that I used so that it was quite clear that the EFFECTS of some of the “weather” items; such as the superheated steam, and the dry ice were of such a nature that they would never be cancelled out by some other extreme weather event; to yield a benign average result.
If you want to believe that we can get the climate by simply multiplying the weather (averaged in some way of course) by a factor; well you are welcome to that point of view.
I wonder what the average of a Hurricane and a Tornado is, in climate science terms of course.
And as to my “credentials” which you see fit to impugn there is more than one bio to be found out there in webland. I didn’t write any of them; so they are probably about like what typical Journalists might write from information they get from various sources. I’m sure if you contacted my alma mater, you could easily get what are the public records; they aren’t secret.
“”” On average you are at a very comfortanble +20 deg C; but if we integrate (add up) the effects of the “weather events”, the results are quite uncomfortable; “””
There you are Merrick; a direct quotation from my post ; exactly what it was I said; that evidently you completely misunderstood. I was extremely careful in the choice of words that I used; I usually am.
It is not my fault if people substitute their own words for mine.
In “other words” lies “other meaning”. So I choose to not use “other words”.
Funny when Mann was attacked because his statistics wasn’t strong enough, this crowd crooned over statisticians. Now this crowd is vilifying them. Can’t have your cake and eat it too, especially when the decadal and interdecadal trends are still pointing up;
http://www.woodfortrees.org/plot/gistemp/mean:10/plot/gistemp/from:1940/trend/plot/gistemp/from:1999/trend/plot/gistemp/from:1994/trend/plot/gistemp/from:1989/trend
sustainableloudoun (18:57:17) wrote:
“this crowd crooned over statisticians. Now this crowd is vilifying them. Can’t have your cake and eat it too,”
That’s an inappropriate analogy. We skeptics are consistently applauding and deriding, respectively, the proper and improper use of statistics. (And we aren’t focusing primarily on the statisticians, but on the way their methodology can be used inappropriately.)
“the decadal and interdecadal trends are still pointing up;”
Sure, because the PDO has a 30-year cycle. Now it’s turning. Hit page-up 12 times and see my post that parses and critiques the statistics and argumentation used in the AP article. In particular, here’s its key point/counterpoint:
“They say that since then, temperatures have fallen — thus, a cooling trend. But it’s not that simple.”
That’s a red herring (diversion). It IS that simple, because a short-term flattening and cooling trend falsifies the IPCC’s prediction for this decade, casting doubt on its models’ reliability; because it casts doubt on the implacability (and the urgency of the threat) of CO2’s alleged “forcing”; and because the PDO has flattened and turned negative at about the same time, which suggests that the PDO is the climate “forcer,” not CO2.
And here’s what I posted as a follow-up to it on another thread on this topic:
“If a patient has a fever and the fever “breaks,” that breakage can’t be waved aside with the diversionary argument that the temperature decline hasn’t lasted long enough to be a long-term trend. No one is claiming it is a long-term trend–-just that the fever (most likely PDO-driven) has broken.”
Very good article by Dr. Briggs. As for you, sustainableloudoun, keep in mind that the criticisms of Mann have more to do with selectively chosen data and improper use of recognized statistical methods in order to draw his conclusions. The sum of all the criticisms in the posts and the observations of Dr. Briggs are not inconsistent with this analysis. Borenstein only gave these stat guys one part of the data, and probably knew these guys well enough to know what kinds of bees were in their bonnet. His article proves nothing other than what we already know – you can cherry pick anything you want to prove what you want to prove.
My basic argument has been, and remains, what is really happening as opposed to what the IPCC and its cronies have predicted would happen based upon their so-called “models?” The answer is that they haven’t even been close. That is the reason for all the “cooling” talk. As George E. Smith pointed out, what is worse is that the surface temperature data that the alarmist scientists use to try to bolster their arguments is highly questionable if not unreliable.
Further, let’s face facts: all climate predictions are shams. Talk of “trends” are useless, and it is time guys like you recognize this. These guys couldn’t predict tomorrow’s weather much less predict long-term climate “trends.”
sustainableloudon
Funny when Mann was attacked because his statistics wasn’t strong enough, this
crowd crooned over statisticians. Now this crowd is vilifying them. Can’t have
your cake and eat it too, especially when the decadal and interdecadal trends
are still pointing up
There is nothing in “being” a statistician that bestows wisdom. It lies in whether the statistical analysis is done properly. If the AP story correctly presents what the “four statisticians” said, there is a great deal missing from the account.
For example, was the only statistical analysis made a simple linear regression on the entire data set? Such an analysis implicitly assumes that the slope (if any) is constant. No one denies that the trend has been generally upward since the ending of the Little Ice Age. But the hypothesis to be tested is that the slope has changed in the past ten years or so. For that, a different sort of test is required.
However, the AP is remarkably silent on what was actually done. Moreover, if the statisticians were not told what the data was, how could one of them then make a statement about temperature trends? Unless they were told afterward. Left unstated is whether they found an upward trend in the past ten years of data and, based on past data, whether such a flat 10-year period is likely.