Saturday, July 13, 2013

Some journalists (and designers) don't understand what science is —and perhaps they never will

Two days ago, Jacob Harris, news hacker at The New York Times, retweeted Yahoo's technology journalist Virginia Heffernan:

I read Heffernan's article and thought that it was a pile of nonsense. My jaw dropped when I found out that she has worked as a fact-checker for the New Yorker magazine.

I snapped:

After she began to get tons of negative feedback, she tweeted:

To which I answered:

I storified the entire conversationCarl Zimmer, who shows up a lot there, is a famous science reporter who has written a bunch of great books.

Heffernan felt offended at some point, and claimed that we were attacking her because of her beliefs. That's not the case. I didn't call her stupid —neither did Zimmer or anyone else,— although her article is indeed stupid, full of logical fallacies, and it reveals a complete ignorance of what science (or evidence, for that matter!) is. That's not an insult. It's proved by the article itself. I felt tempted to invoke the old "everyone is entitled to their own opinions, but not to their own facts" saying, and Voltaire's "the interest I have in believing in something is not a proof that the something exists." Jerry Coyne has written a detailed rebuttal of the article, if you're interested.

This whole affair made me feel a bit sad. Even if Heffernan's piece is an extreme example, it's also a reminder of misconceptions commonly heard in newsrooms. The main one is the pervasive sentence "science says...", which makes me cringe whenever I hear it or read it. Science is not just a body of knowledge but, first of all, a tool to create knowledge, a method, a systematic process of devising approximate, always amendable, explanations of reality. Science doesn't "say" anything. People who use science do.

And science is not that hard to understand —something that Heffernan suggests— as it is based on a very simple loop (could we call it an algorithm?): You pose a conjecture, you criticize it and test it —you verify it and, more importantly, try to falsify it— and, if everything goes well, you will be able to propose an explanation. That explanation can lead you or your peers to new informed conjectures, and the process continues.

The key is that those explanations, no matter how well grounded, can and will always be discarded. Old theories are substituted by new ones all the time. That doesn't necessarily mean that old theories were bad theories, only that they were not as good —accurate— as the new ones. As Carl Sagan once wrote, "science is a self-correcting process." Scientific hypotheses and theories, by the way, are not "stories" —again, something that Heffernan doesn't understand,— neither are they simple, casual, or random guesses. Those words have precise meanings.

Why does all this matter? Why did some of us spend so many hours in a conversation with a person who used Michel Foucault'ouvre (!) as an argument from authority? For the same reasons I wrote this. Because we —journalists and visualization/infographics designers— have a responsibility: Our job, as Jeff Jarvis has written, is "a service whose end is an informed public." Heffernan's article has the potential to misinform thousands, if not tens of thousands. We need to be methodical and know a bit about science, logic, Math, and statistics, whether we like it or not ("But I studied Journalism because I want to be a writer!"; "But I studied design because I want to do cool-looking stuff!") Months ago, I suggested readings about statistics. Here you have a few about science in general; these are personal favorites of mine among recent books:

1. The first chapter of Donald R. Prothero's Evolution: What the Fossils Say and Why It Matters.

2. The first 100 pages (more or less) of David Deutsch's The Beginning of Infinity: Explanations That Transform the World.

3. Michael Shermer's The Believing Brain: From Ghosts and Gods to Politics and Conspiracies---How We Construct Beliefs and Reinforce Them as Truths. The entire book, please, which is essential to sharpen your bullshit detector.