I really should try to automate more, but sometimes it’s hard to avoid old-fashioned sequence gazing.

The WSJ authors’ main point is that if the data doesn’t conform to predictions, the theory is “falsified”. They claim to show that global mean temperature data hasn’t conformed to climate model predictions, and so the models are falsified.
But let’s look at the graph…

All climate models include parameters that aren’t known precisely, so the model projections have to include that uncertainty to be meaningful. And yet, the WSJ authors don’t provide any error bars of any kind! The fact is that if they did so, you would clearly see that the global mean temperature has wiggled around inside those error bars, just like it was supposed to.

So before I go on, let me be blunt about these guys. They know about error bars. They know that it’s meaningless, in a “noisy” system like global climate, to compare projected long-term trends to just a few years of data. And yet, they did. Why? I’ll let you decide.

And there’s more over at Physics Today.

Eugenie Scott has a guest post up at Real Climate on why the NCSE has taken up the issue of climate change in the classroom:

Imagine you’re a middle-school science teacher, and you get to the section of the course where you’re to talk about climate change. You mention the “C” words, and two students walk out of the class.

Or you mention global warming and a hand shoots up.

“Mrs. Brown! My dad says global warming is a hoax!”

Or you come to school one morning and the principal wants to see you because a parent of one of your students has accused you of political bias because you taught what scientists agree about: that the Earth is getting warmer, and human actions have had an important role in this warming.

From the Guardian:

McCarthy has long enjoyed a close interaction with scientists, keeping an office at the Santa Fe Institute, an independent research centre that also houses a host of scientists, founded by the Nobel prize-winning physicist Murray Gell-Mann. (George Johnson’s biography of Gell-Mann, Strange Beauty, reveals that McCarthy performed a line-edit on the entire manuscript of his The Quark and the Jaguar, “but Gell-Mann was too rushed and disorganised to take advantage of the suggestions”).

Gell-Mann disorganized… that makes me feel better about my own work habits. On the other hand, Gell-Mann is a genius.

Jeffrey Eugenides’ latest novel The Marriage Plot features a bipolar yeast geneticist. While writing the book, Eugenides, who lives down the road from several world-famous yeast genetics labs at Princeton, decided to do a little research. He visited David Botstein, one of the elder eminences of yeast genetics, got a tour of the lab, and nobody there seems to remember the visit. From the New York Times:

Dr. Botstein took his visitor into his lab and announced, “I have here a novelist.”

The lab manager, Sanford Silverman, introduced Mr. Eugenides to the scientists there, and they took him in hand. Mr. Eugenides asked how someone would ruin an experiment. By mixing up the specimens, they told him. So in the book, Leonard does just that.

In an interview, Dr. Botstein said he could not have spent much time with Mr. Eugenides, because the visit had made no impression on him.

“I never heard of the book, and I don’t remember talking to the guy,” Dr. Botstein said.

Dr. Silverman remembers Mr. Eugenides coming to the lab, but does not recall details of his visit.

C.P. Snow is turning in his grave.

Ars Technica, The Tenure Track Not Taken:

Becoming a university professor requires a lot of work for very little financial reward, compared to most other professions. In STEM (science, technology, engineering, math) fields, the minimum requirement is four years of undergraduate education, plus anywhere between four and a half and eight years of graduate studies, followed by an (ever increasing) number of years of post-doctoral work. That may get you an assistant professorship where, at a state university, the starting salary is in the $60k-70k range.

(The only other career path I have seen that has similarly low pay for exorbitant requirements is becoming a chef. In both cases, you only do them because you simply love doing them.)

To be fair, starting salaries in the life sciences at major research universities are better than 60-70K, and are in fact competitive with starting industry salaries. Why do I do this? ‘Because I love it’ is a little too trite. This can be a desirable career because 1) the autonomy available in an academic research career is hard to beat unless you’re independently wealthy, and more importantly, 2) there actually aren’t that many places outside academia where you do science the way you imagined it would be as a naive undergraduate, that is, defining your own interesting questions and going about solving them. In most (but not all!) industry settings, you will spend most of your time working on a research question defined by someone else.

It is difficult to read. The page is dark.
Yet he knows what it is that he expects.

The page is blank or a frame without a glass
Or a glass that is empty when he looks.

The greenness of night lies on the page and goes
Down deeply in the empty glass…

Look, realist, not knowing what you expect.
The green falls on you as you look,

Falls on and makes and gives, even a speech.
And you think that that is what you expect,

That elemental parent, the green night,
Teaching a fusky alphabet.

- Wallace Stevens

Stevens is not easy, but he repays the effort with his remarkable word choice and fierce cognitive engagement. Of particular interest to the scientifically inclined, his poems are often about the intersection between our minds and reality – clearly a theme in this poem. (Maybe this poem is also about you, trying to read this poem.) As he once wrote, “Poetry is a response to the daily necessity of getting the world right.”

It’s short and sweet with simple lyrics and a straight forward structure. Like all the best punk, though, Duffy and the Doubters’ Spider Baby Jesus is great not because it’s catchy but because there’s something true about it. It’s the kind of song that works because it leaves you knowing that something, anything, somehow makes more sense than it did before. This time around, Vancouver music mainstay Duffy Driediger reminds us of the pitfalls of relying on the same tired narratives. When we tell the same stories, the ending is always the same. There’s always a boring subject and there’s always someone else to blame. It may be inescapable. It’s always the same old song.

Science can’t just tell the same old stories. Assumptions have to be challenged, new questions have to be asked. Yesterday Josh wrote about a study of the adaptive value of prejudice, crippled by its reliance on the assumption that acting on prejudice is effective: Prejudice is rational if you assume prejudice is rational. Discussions in science education often face the same problem. Generation after generation there are calls for improving science education by actively involving students in doing science and learning its processes to get away from traditional textbook and rote learning. The funny thing is hardly anyone has ever advocated it as the way science should be taught. The problems in implementation are numerous and challenging, from evaluation structures to school facilities, but there’s no way these problems can be addressed when discussions continue to focus on convincing everyone that process-focused science teaching is a good idea. When it’s always the same old stories, the ending is always the same.

Science, both doing it and learning it, can’t work that way. What better way to be reminded than with a great song.

Duffy and the Doubter’ Scriptural Supplies is available in limited edition and for download at Triple Crown Recordings. Driediger also fronts the always terrific band Ladyhawk.

Changizi is never short of interesting ideas, and a researcher should always make the strongest case for their ideas that they can. Unfortunately, I have some issues with the evidence supporting that “strongest case” and the way he presents it:

Assumption of natural selection. It is a common fallacy to assume that the existing features of any organism must be the product of natural selection. This is, in part, a result of the influence of the success Richard Dawkins’ books, such as The Selfish Gene, which focuses heavily on selection. Selection, however, is but one of several forces of evolution (i.e., drift, mutation, and migration) and should not be preferred without compelling evidence. Selection is not a legitimate null hypothesis. Starting from the position that every interesting feature requires a natural selection based explanation is not in accord with current evolutionary theory or the scientific method.

Natural selection as the “designer”. The language is evocative, and as such, is not technically flawed. It does have the effect of making one sound eerily like an employee of the Discovery Institute and suggests that the desire for selective explanations is motivated by a similar conviction that biological systems are too complicated to have evolved by chance.

Ask for evidence. In response to requests for evidence testing his theories, Changizi provides evidence that his theories are plausible. This is good, but not the same as direct testing. The theories are not truly predictive either. They are not predicting unobserved phenomena. They do have the feature of being consistent with previously observed phenomena. A predictive theory should suggest an experimental design we could use to evaluate it. His neurobiology brings to mind string theory. It is mathematically plausible. It is consistent with prior observations. We have no ability to test it.

Is “Why?” > “How?” I like “why” questions; but this is not an “either/or” situation. Understanding “how” a system works fundamentally constrains the set of reasonable answers to “why?”. Theories can get predictions right at a certain level of resolution, but may fall apart or impede progress as we learn more. Newton’s approach to physics remains wonderfully predictive for macro objects; but he got the “how” wrong. Learning more about the “how” (i.e., particle physics and quantum mechanics) is what enabled the microelectronic revolution and this post to be written.

Or, to be more on point, Darwin developed a great theory describing “why” we see the diversity of life that we do. He, however, got “how” variation was created and passed on terribly wrong. Initially, this was not that important to natural selection or evolutionary theory. Eventually, the insight provided by Mendel’s genetic discoveries was necessary to develop evolutionary biology as we know it today.

Just-So Stories. It is one thing when an evolutionary biologist of Michael Lynch’s reputation lectures his fellow scientists about spinning evolutionary “Just-So Stories” to make their papers appear more interesting. It is another thing to have the critique come from someone who has published a theory that is actually one of Kipling’s* Just-So Stories.