On models and misunderstandings

Folks commonly misunderstand what the term ‘model’ means in science, particularly those operating from a particular theological or ideological model of the world that leads them to attack mainstream conclusions in climate science or evolutionary biology. This confused comment attacking climate models is fairly typical:

Extrapolation is not fact. It is estimate. And the accuracy is in the eye of the beholder. So if they [the North Carolina legislature] want to legislate HOW to estimate, it is far less controversial than you make it sound. You base estimates on past experience, not models, which is what climate change is really based on, not fact.

This person is lacking a coherent notion of what extrapolation, estimate, model, and fact mean in science. Reading the comment in context, this person seems to be defending the idea that a linear fit to your data which you use to make predictions is “extrapolation” from past experience, not a model, and is a more reliable way to do science than using a model. To be fair, this confusion is common, and in my experience the role of models in science is not generally taught well in schools. So let’s talk about the role of models in science. Continue reading “On models and misunderstandings”

It would be nice if you could legislate away reality…

…but that usually doesn’t end well. Scott Huler at Plugged In reports on the futile attempts of North Carolina legislators and members of a developer’s lobbying group to legislate away a possibly catastrophic sea level rise by making non-linear scientific models illegal:

That is, the meter or so of sea level rise predicted for the NC Coastal Resources Commission by a state-appointed board of scientists is extremely inconvenient for counties along the coast. So the NC-20 types have decided that we can escape sea level rise – in North Carolina, anyhow – by making it against the law. Or making MEASURING it against the law, anyhow. Continue reading “It would be nice if you could legislate away reality…”

Mary-Claire King describes what makes a good scientist

About Svante Pääbo and Alan Wilson, quote in this must-read piece on Neanderthals and genomes:

“Each of them thought of very big ideas,” she told me. “And each of them was very good at translating those ideas into testable hypotheses. And then each of them was very good at developing the technology that’s necessary to test the hypotheses. And to have all three of those capacities is really remarkable.” Also, although “they were both very data-driven, neither was afraid to say outrageous things about their data, and neither was afraid to be wrong.”

If you’re never wrong in science, you’re not generating enough ideas.

Book club: It’s a digital world and we just live here

Welcome to the first Finch and Pea Book Club. Grab your favorite brew and pull up a chair. Our inaugural book is George Dyson’s recently published Turing’s Cathedral. Have you read the book? Got an opinion? Let’s hear about it in the comments.

On the eve of World War II, when much of the world was beginning to mobilize its industrial and scientific resources in preparation for yet another exercise in mass slaughter, Abraham Flexner, the driving force behind the modernization of America’s higher education, wrote a plea for basic research, “The Usefulness of Useless Knowledge” (PDF). Flexner argued that much of the transformational technology on which our society relies is the consequence of esoteric, abstract, curiosity-driven scientific research that was conceived without specific, practical applications in mind. George Dyson’s Turing’s Cathedral is the story of how the useless knowledge of abstract mathematics and logic led directly to the birth of today’s digital, computerized society, in the boiler room of that most pallid of ivory towers, the Princeton Institute for Advanced Studies. Continue reading “Book club: It’s a digital world and we just live here”

I’ve always had a hunch that this was true…

Retracted Science and the Retraction Index:

A plot of the journal retraction index versus the impact factor revealed a surprisingly robust correlation between the journal retraction index and its impact factor (P < 0.0001 by Spearman rank correlation) (Fig. 1). Although correlation does not imply causality, this preliminary investigation suggests that the probability that an article published in a higher-impact journal will be retracted is higher than that for an article published in a lower-impact journal.

The charitable interpretation is that high-impact journals are willing to take higher risks in exchange for a bigger splash. And of course there is a not-so-charitable interpretation… a focus on big splash and getting a big scoop trumps scientific rigor.

(h/t io9)