So says E.O. Wilson in the Wall Street Journal.

But don’t just read the headline – be sure to catch the nuance in Wilson’s piece. He’s saying don’t let fear of math drive you from science, because you don’t need straight A’s through four semesters of calculus to be a good scientist.

I don’t quite agree with Wilson when he says you can always find a mathematician as a collaborator to handle the math you need. A mathematically illiterate biologist working with a biologically illiterate mathematician is usually not a fruitful combination. But good scientists pick up the necessary mental toolkit as it’s needed, including mathematical and statistical knowledge (as long as they’re willing to put some serious effort into gaining that knowledge, as opposed to, say, figuring out how to mindlessly apply t-tests).

Sean Eddy calls this approach “ante-disciplinary science”:

I’ve been a computational biologist for about 15 years now. We’re still not quite sure what “computational biology” means, but we seem to agree that it’s an interdisciplinary field, requiring skills in computer science, molecular biology, statistics, mathematics, and more. I’m not qualified in any of these fields. I’m certainly not a card-carrying software developer, computer scientist, or mathematician, though I spend most of my time writing software, developing algorithms, and deriving equations. I do have formal training in molecular biology, but that was 15 years ago, and I’m sure my union card has expired…

Progress is driven by new scientific questions, which demand new ways of thinking. You want to go where a question takes you, not where your training left you…

New science needs to be judged on its merits, not by the disciplinary credentials of the people doing it—particularly in fast-moving interdisciplinary areas where any formal training may be outdated anyway. If your grant proposal includes statistical analysis, your reviewers shouldn’t be acting as enforcers requiring you to have a card-carrying statistician as a collaborator. Maybe in your narrow area, you know how to do the relevant statistics as well as any formally trained statistician…

This approach to science, embodied in Eddy’s career, should not be confused with the half-assed, sloppy science that occurs when wet lab or computational biologists just try to wing it, thinking that computational analysis is just running someone else’s software or bench work is like following the directions on a box of Betty Crocker cake mix.

Back to E.O. Wilson – if you feel that math is not your strong point, realize that most scientists in most disciplines feel the same way. The solution is to suck it up and deal with some math, but don’t let it stop you from pursuing a subject that fascinates you:

If your level of mathematical competence is low, plan to raise it, but meanwhile, know that you can do outstanding scientific work with what you have. Think twice, though, about specializing in fields that require a close alternation of experiment and quantitative analysis. These include most of physics and chemistry, as well as a few specialties in molecular biology…

For aspiring scientists, a key first step is to find a subject that interests them deeply and focus on it. In doing so, they should keep in mind Wilson’s Principle No. 2: For every scientist, there exists a discipline for which his or her level of mathematical competence is enough to achieve excellence.

It seems that, here, we are talking about the discomfort with math or lack of expertise in math. There is a related, but different issue with the actual fear of math that can be created in people from the way it is taught, such as having to do problems “at the board” when young. To my mind, math is kind of like dogs. You can succeed in a world (science) filled with dogs (math) even if you don’t like dogs (math), but you are going to have a really hard time if dogs (math) scares you poopless.