William M. Briggs

Statistician to the Stars!

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Minds Have Become So Rotted By Global Warming They Listen To Naomi Klein

Artist's conception of Naomi Klein making her most reasoned argument.

Artist’s conception of Naomi Klein making her most reasoned argument.

Two weeks Northern Michigan vacation and I still can’t escape.

Anti-capitalist activist, Vatican invitee, non-scientist, and authoress of extremist political tracts Naomi Klein said global warming will make Australia “meaner”.

I do not joke nor jest. She said, “You see that in Australia where the treatment of migrants is a profound moral crisis. It’s clear that as sea levels rise that this mean streak and open racism is going to become more extreme — climate change is an accelerant to all those other issues.”

These are statements of such profound stupidity that I’m rather taken aback. I want to be nice, to be charitable, to find something redeeming in Klein. Brother, this is as nice as I can get. An increase in meanness. What’s next? Headlines blaring “Global Warming Causes Cooties”?

That The Guardian, the paper in which these remarks fell, took them seriously, is not surprising. That paper is willing to say anything to advance its agenda. For them, the ends justify the means—an inherently evil position.

Now we skeptics often joke about this, but apparently it’s true. A environmentalist fanatic can say whatever they want about global warming and it will be taken not only seriously by the bien pensant but as proved true just for the stating.

This isn’t science, it isn’t even religion. To say it is gross superstition is an understatement. Nothing short of crazed monomania can be the explanation. Klein is so incensed that people are largely free to make their own economic decisions, decisions she feels (not thinks) would be better made by her and her confreres, that she is willing to emit streams of preposterousities (you heard me: preposterousities), knowing the resulting puddles will be lapped up eagerly by her ideological followers.

Klein’s trick is to say something mind-numbingly stupid, perhaps even something that she herself doesn’t believe, and then wait for her friends in the media to parrot it, and in the process cover the statement with a layer of reportorial seriousness.

The sane are then forced to counter the idiocy by saying things like, “Global warming can’t cause ‘meanness’ because meanness can’t possible be related to temperatures”. But engaging in that very scientific act merely reinforces the error among the vulgar and ideologically blinded, who are like that dog in the Far Side cartoon who while listening to a stream of words from his owner only hears his name. The ignorant will only hear “global warming”, “meanness”, etc.

On the other hand, if you do what I am now doing you will be accused by the ignorant of being—wait for it—mean. And told you haven’t answered the charges.

What a circus! Science and truth have been left so far behind in the dust that I’m not even sure a majority of our populace would recognize it.

Trenberth Is Wrong About Global Warming: The PDO Is An Effect, Not A Cause

From the paper's Figure showing the PDO and odd regression.

From the paper’s Figure showing the PDO and odd regression.

Kevin “Travesty” Trenberth had a peer-reviewed article in Science entitled, “Has there been a hiatus? Internal climate variability masks climate-warming trends.

First, the word “hiatus” is wrong. Using it assumes what it seeks to prove: that the atmosphere is warming substantially because of human activity. We do not know this is true; and given model results, an area where Trenberth treds oh so lightly, it is almost surely false. The word “hiatus” implies the warming is there, but has been “masked” or “beaten down” by other causes such that the total cause is a no-warming signal in the (operationally defined) global mean surface temperature (GMST).

The real question of interest is not whether there was a “hiatus” but what are the main causes of the (value of the) GMST? Some of the causes Trenberth mentions are uncontroversial; for instance, volcanic eruptions, which block incoming solar radiation. But one cause he mentions, and which is says is responsible for the “hiatus”, is not a cause at all. This is the Pacific Decadal Oscillation (PDO).

He says, “Observations and models show that the PDO is a key player in the two recent hiatus periods”. He cliams the PDO is responsible for “interannual variability” of the atmosphere. This cannot be so. The PDO is an effect, an observation. It is not a primary cause. The PDO is not something apart from the atmosphere, independent of it and which only shows up every so often. It is a pattern formed in the atmosphere by the same (and other) causes which are responsible for the GMST value.

And the same is true, of course, for the El Nino, La Nina, AMO, and any other human-identified handy pattern. To say the PDO is a cause is like saying the “pattern” of colder temperatures we notice in December in the northern hemisphere are responsible for (a.k.a. cause) winter.

Trenberth skirts around the lack of skill exhibited by climate models and implies the models would have been right—which means he acknowledges they were wrong—had this nasty PDO not had its way with the atmosphere. Such faith. He says, “the associated changes in the atmospheric circulation are mostly not from anthropogenic climate change but rather reflect large natural variability on decadal time scales. The latter has limited predictability and may be underrepresented in many models”.

This is silly. The models claimed to be able to identify the main causes of atmospheric change. Because the predictions were so awful is proof that this claim is false. We do not know all the main causes of atmospheric change. If we did, our forecasts would have been accurate.

As I said, the main causes of the changes in the atmosphere also cause changes in the man-identified pattern we call the PDO. We also do not do a stellar job of predicting the PDO. More evidence we do not understand all the causes of the changes in the atmosphere.

Further, there is no such thing as “natural variability”. It doesn’t exist like volcanoes and even human carbon dioxide emissions do. Natural variability is a measure, the result of us holding up a sort of yardstick to the atmosphere. The yardstick exists all right, but it has no causal influence of the atmosphere itself.

For being a world-renowned expert on our climate, Trenberth certainly speaks poorly of its operation.

Small points: Trenberth ignores the satellite data temperature record and instead relies on a statistical reconstruction which does not show the uncertainty in its estimates. He smooths his “data” to show us black lines which are not the “data”, and then speaks of these lines as he speaks of “natural variability”, i.e. as if it’s something real. And then he does some odd ad hoc piece-wise linear regression the purpose of which is unclear and, as far as I can tell, is of no use whatsoever, i.e. it makes no predictions like all good statistical models should.

Stream: No, Half of British Youths Aren’t “Bisexual”


Today’s post is at the Stream: No, Half of British Youths Aren’t “Bisexual”.

A survey by YouGov in Great Britain recently announced that “1 in 2 young people say they are not 100 percent heterosexual.” This headline betrays an enormous confusion in our culture’s understanding of human nature, and the language we use to refer to ourselves.

The survey makers asked respondents “to plot themselves on a ‘sexuality scale’,” a pseudo-scientific quantification of desire invented by the disturbing and unreliable Alfred Kinsey. The results were that “23 percent of British people choose something other than 100 percent heterosexual– and the figure rises to 49 percent among 18-24 year olds.”

But there is a difference between human sexuality and human desires and behavior. As I hope to demonstrate, confusing one for the other has been, and increasingly will be, a source of much grief in our society.

Human Nature

The nature or essence of a human being is to be sexually reproductive…

Go there to read the rest.

Another in a long series of consequences of abandoning Christianity.

Is Young Fatherhood Causally Related To Midlife Mortality? Wee P-values Say Yes!

Table 1 from the paper.

Table 1 from the paper.

The title of today’s post is culled from the peer-reviewed paper “Is young fatherhood causally related to midlife mortality? A sibling fixed-effect study in Finland” by Elina Einiö and two others in the Journal of Epidemiology and Community Health. The question the title poses is answered “Yes.”

What is it with these academic attacks on marriage? Last week having a kid was said to be worse than death. “Having A Kid Worse Than Divorce Or Death? Wee P-values Say Yes.” Yesterday another academic asked, “Two thirds of married people admit to or desire an affair. Is it time to rethink sexual morality?” She says yes. (At least she didn’t abuse any p-values.) And everything about this making-the-rounds “Tinder-hookup culture” article is depressing.

What is striking in today’s academic foray is the word “causally” in the title. Wee p-values are being used to claim a causal relationship, which is exactly the wrong thing to do (video). It might be true that having kids young kills men, but proving it via wee p-values cannot be done.

It also sounds preposterous to claim that young men fathering children kills those men. That’s, after all, what “mortality” means. Kills. Rubs out. Knocks on the head. Sends to the Great Beyond. Being “at risk” for mortality because of having kids early means that the act of fathering is somehow killing some men.

Now this study looked at a sample of Finns. The authors followed men from age 45 to death or age 54, at which time they were “censored” (in the usual statistical way). All-cause mortality or censoring was the end point.

That’s a common approach, but it’s a silly one. Bus runs over a man, which is a cause of death (no p-value needed to confirm). If that man fathered a child young, in this database it was counted against him as a death not being caused by the bus but because he was a young father. In order for this to be true, it has to be that this young father walked (or was pushed? or slipped? it’s Finland, after all) in the bus’s path because he had a kid before gray hair set in. That sound plausible?

No. It doesn’t sound impossible, of course. But it is implausible. Especially when you consider the same thing can and must be said for every other “mode” of death. And listen, since it is obvious that the authors are wrong and that young fatherhood in and of itself isn’t killing men, we’re not after direct causes, but causes of the cause of the death. A cause of the cause of death in the bus example is that a young father was forced to take a bus to work because he was a young father.

How could this happen? Well, I don’t know, but that is, of course, no proof that it cannot.

Here’s the conclusion: “Men who had their first child before the age of 22 or at ages 22–24 had higher mortality as compared with their brothers who had their first child at the median or mean age of 25–26.” Smells like an arbitrary cut-off, no? Like maybe, just perhaps—I make no accusation—that ages were played with until a wee p-value from the model came forth.

But this is ungenerous. Nobody really hunts for wee p-values, right? The real story is in their Table 1, which is reproduced at the top of this post.

More (but not all) fathers under 22 had only “basic” education, more were unmarried, more were divorced than older fathers. This suggests it’s not so much fatherhood which killed the 6.6% of the young men, but other activities. What might these be?

We have no idea, at least, no idea from this data set. For, you (don’t) see, the authors never examined the stated or measured cause of death in any case. They should have—but that’s too much work!

This is a very important point, which we must repeat. Something caused each young and each old father to die (of those who died, naturally). If we say it is young fatherhood that is killing some young men, then it must be something else that killed the old fathers. What was that or what were those causes? Why and how did they differ?

The problem with classical statistical analysis is that it substitutes formulaic manipulation for hard work and hard thinking. And it’s a lousy substitute.

What is needed is (A) proper understanding that statistics can’t prove cause, and more importantly than anything else (B) a new (old)—dare we say a third?—way of doing analyses.


Thanks to KA Rogers for alerting us to this article.

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