At the beginning of April (but not on April Fool’s Day), the Wall Street Journal published an essay by E.O. Wilson (a famous biologist): Great Scientists Don’t Need Math. The gist of the article is that Dr. Wilson never learned much math and did well in biology, so others can do so also:
Wilson’s Principle No. 1: It is far easier for scientists to acquire needed collaboration from mathematicians and statisticians than it is for mathematicians and statisticians to find scientists able to make use of their equations.
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.
The first principle is probably true, but is more a sociological statement than one inherent to the disciplines: applied mathematicians and statisticians welcome collaborations with all sorts of scientists and are happy to learn about and work on real problems that come up elsewhere, while biologists (particularly old-school ones like Dr. Wilson) tend not to be interested in anything outside their own labs and those of their close collaborators and competitors.
The second principle is possibly also true, though much less so than in the past. Biology used to be a major refuge for innumerate scientists, but modern biology requires a really strong foundation in statistics, far more than most biology students are trained in. The number of positions for innumerate scientists is rapidly shrinking, while the supply of innumerate biology PhDs is growing rapidly. In the highly competitive job market for biology research, those who follow E. O. Wilson’s advice have a markedly smaller chance of getting the jobs they desire. Of course, Dr. Wilson seems to be unaware of the decades-long oversupply of biology researchers:
During my decades of teaching biology at Harvard, I watched sadly as bright undergraduates turned away from the possibility of a scientific career, fearing that, without strong math skills, they would fail. This mistaken assumption has deprived science of an immeasurable amount of sorely needed talent. It has created a hemorrhage of brain power we need to stanch.
An undergrad degree in biology (even from Harvard) has not gotten many students much more than low-level technician jobs for most of that time (admission to grad school is the better option, as biology PhDs have been able to get temporary postdoc positions at least). Perhaps Dr. Wilson considers a dead-end job at little more than minimum wage a suitable scientific career—many others do not.
Dr. Wilson does make one unsubstantiated claim that I agree with:
The annals of theoretical biology are clogged with mathematical models that either can be safely ignored or, when tested, fail. Possibly no more than 10% have any lasting value. Only those linked solidly to knowledge of real living systems have much chance of being used.
Biology is a data-driven science, not a model-driven science (a distinction that physicists trying to jump into the field often miss). Most of “mathematical biology” has been an attempt to apply physics-like models in places where they don’t really fit. But there has been a big change in the past 10–15 years, as high-throughput experiments have become common in biology. Now mathematics (mainly statistics) is needed to make any sense out of the experimental results, and biologists with inadequate training in statistics end up making ludicrously wrong conclusions from their experiments, often claiming high significance for random noise. To understand the data requires more than Wilson’s “intuition”—it requires a solid understanding of the statistics of big data and multiple hypotheses, as humans are very good at perceiving patterns in random noise.
I was pointed to Dr. Wilson’s WSJ essay by Iddo Friedberg’s post Terrible advice from a great scientist, which has a somewhat different critique of the essay. He accuses Wilson of “not recognizing the generalization from an outlier cannot serve as a viable model, or even an argument to support his position.” Iddo makes several other points, some of them the same as mine—go read his post! Of course, like me, Dr. Friedberg is a bioinformatician and so sees the central role of statistics in 21st century biology. Perhaps the two of us are wrong, and innumerate biologists will again have glorious scientific careers, but I think the odds are against it.