Yesterday we looked at NCDC’s claim that the 13-month stretch of “above-normal” temperatures had only a 1 in 1.6 million chance of occurring. Let’s today clarify the criticism.

The NCDC had a list of premises, or evidence, or assumptions, or some model which they assumed true. Given that model (call it the Simple Model), they deduced there was a 1 in 1.6 million chance of 13-in-a-row months of “above-normal” temperatures. This probability, given that model, was true. It was correct. It was right. It was valid. Everybody in the world should believe it. There was nothing wrong with it. *Finis*.

However, the intimation by the NCDC and many other folks was that because this probability—the *true* probability—was so small, that therefore the Simple Model was false. And that therefore rampant, tipping-point, deadly, grant-inducing, oh-my-this-is-it global climate disruption on a unprecedented scale never heretofore seen was true. That is, because given the Simple Model the probability was small, therefore the Simple Model was false and another model true. The other model is Global Warming.

This is what is known as backward thinking. Or wrong thinking. Or false thinking. Or strange thinking. Or just plain silly thinking: but then scientists, too, get the giggles, and there’s only so long you can compile climate records before going a little stir crazy, so we musn’t be too upset.

Now something caused the temperatures in those 13 months to take the values that it did. Some string of physics, chemistry, topography, whatever. Call this whatever the True Model; and call it that because that is what it is: it is the true cause of the temperature. Given the True Model, then, the probability of the temperature taking the values it did was 1—100%. We can only add *of course*.

The Global Warming model is a rival model held by many to be unquestionable (which is not to say *true*). Why not ask: given the Global Warming model, what is the probability of 13-in-a-row “above-normal” temperatures? Nobody did ask, but let’s pretend somebody did. There will be some answer, some probability. Save this and set it aside. This probability will also be true, correct, right, assuming we believe the Global Warming model is true.

Yet there also exists *many* other rival models besides the Global Warming and Simple Models. We can ask, for each of these Rival Models, what is the probability of seeing 13-in-a-row “above-normal” temperatures? Well, there will be some answer for each. And each of those answers *will be true*, correct, *sans reproche*. They will be right.

Now collect all those different probabilities together—the Simple Model probability, the Global Warming probability, each of the Rival Model probabilities, and so on—and do you know what we have?

A great, whopping pile of nothing.

What we have are a bunch of probabilities that aren’t the slightest use to us. Get rid of them. Consider them no more. They will do us no good. And why should they? All they are, are a group of *true* probabilities, each calculated assuming a different model was true.

But we want to know *which model is true*! The probabilities are mute on this question, silent as the tomb. We ask these probabilities to tell us which model is true (or closest to the True Model) but answer comes there none. Actually, the answer will be, “Why ask me? I’m just a valid probability calculated assuming my model was true. I have no idea whether my model, or any other model, is true.”

Here is what we *should* ask: Given we have seen 13-in-a-row “above-normal” temperatures, and given my understanding of all the rival models, what is the probability that any of these rival models is true?

So if somebody tried to answer that question with a “I don’t know. But I do know that if I assume the Simple Model is true, the probability of seeing the data is this-and-such” you would be right to find that person a comfortable chair and to lecture him gently on the advantages of decaffeinated coffee.

Last thrust: assume the Simple Model is the best model there is. Once more, the probability of seeing the data we saw is small. But so what? Rare things happen all the time (see yesterday’s example). People win the lottery, which has a smaller probability than seeing the temperatures we say. If the Simple Model is the best we have, then all we can say is that we have seen a rare event. And this *should be cheering news!* Especially if you did not enjoy 13 months in-a-row of “above-normal” temperatures. For we have just learned that such events are rare, and that things almost certainly return to “normal.”