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Terrible Advice From a Great Scientist
Shipud writes "E.O. Wilson is the renowned father of sociobiology, a professor (emeritus) at Harvard, two time pulitzer prize winner, and a popularizer of science. In a recent article in the Wall Street Journal, Wilson provides controversial advice to aspiring young scientists. Wilson claims that math literacy is not essential, and that scientific models in biology, intuitively generated, can later be formalized by a specialized statistician. One blogger calls out Wilson on his article, arguing that knowing mathematics is essential to generating models, and that lacking what Darwin called the "extra sense" is essentially limiting to any scientist."
Math, intuition, and insight are all important. But they don't all have to come from the same person. I have worked on plenty of teams where the creative work and number crunching tasks were delegated to different people. I am currently working on a 3D educational game, using OpenGL. It involves lots of gnarly trig and vector math, which I am good at. It also involves lots of creative scene design and character development, which I am not good at. So I work with an artsy chick, and we make a good team. I don't see why splitting creativity and implementation shouldn't work for biology as well.
Ssh, we're busy crafting a strawman here, and you're just trying to blow it down!
Sure, the roles do require "math literacy" which is a lower standard than "sufficient mathematical and comptuational capability to independently produce results for a research journal."
Just like natural language literacy is a lower standard than powerful, skilled writing.
My 'aunt', who still works for a pharmaceutical firm analyzing statistical analyses by researchers, would snort tea out her nose reading this. Doing the research, finding a useful drug, doing minimal testing, and then concocting the analysis to fit the very limited empirical model is not uncommon in the drug industry. Her job was and is to study that 'analysis', identify any problems, send it back for improvement, and repeat until either the researchers give up and move on to something they can demonstrate is effective AND safe enough for the market, or succeed and are able to show provable, reliable results.
Wilson would not like herm, and for good reason - she would call his methods little more than guessing. She has proven repeatedly that well-meaning researchers can find some statistician to lend unwarranted credence to imaginary results.
Kinda sad that this passes as science at all. Wilson seems, to me, to be stating that research need not be proven, merely justified.
deleting the extra space after periods so i can stay relevant, yeah.
The math behind quantum physics and relativity is of secondary importance compared to the phenomena they predict and define. Einstein had the insight that everything must be relative, and the math followed from that. Mathematicians merely model nature based on existing insights. But it are these insights that create new science and discoveries, and not the math that models them.
My karma ran over your dogma
Collecting data without having a darn good grasp of how the data analysis works is a great way to waste a huge amount of time and money collecting mostly useless data. It may not be the same person doing both, but the data-collector definitely needs to be intimately "in the loop" about how their experimental work impacts uncertainties in the final analysis.
Without understanding the measurements and statistics involved, the experiment design will most certainly turn out to be crap.
Here, fixed that for ya.
Ezekiel 23:20
I was a natural sciences major in college and what you're talking about is one or maybe 2 classes worth of math. You don't need calculus or anything beyond that in most cases to design an experiment, obviously depending upon the particular field of study. Statistics itself is heavily derived from a set of formulas that you can look up in a book and the reasoning behind it requires at most intermediate algebra to understand.
I definitely agree that you need an understanding of statistics to design your experiments, but really, the amount of math you really need is surprisingly small given that you're going to want to bring in an expert that's experienced in the specific area you're working anyways. Now, were we to go back in time to days when there wasn't a huge team, that would presumably be a different matter. But, understanding doesn't really require that much math.
TL:DR, you're going to want an expert in dealing with modelling and data of the type you're looking at. It makes more sense than reinventing the wheel every time you do an experiment and forcing people to master not just one specialty, but several of them, and ultimately it's unlikely that they'll achieve a level high enough to compete with the best in both fields.
You make a very strong point. There are often statistical and mathematical modeling assumptions that the researchers are aware of ahead of time, subtle pitfalls in the experimental setup that must be avoided to produce the type of data needed, etc., that the technicians/engineers will be unaware of unless the researchers themselves are directly involved in the experiments. By the same token, it's a good idea to have an engineer involved in the data collection review the research prior to publication to catch any obvious flaws in the modeling assumptions or misuse of the data (even if he doesn't understand everything in the paper). 'Separation of duties' is something that comes from laziness or time/budget constraints rather than being a template for solid scientific work.
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Don't delude yourself: This is anti-intellectualism. Sociobiology has issues, but that's not because it's got "socio" in the name.
It's because evolutionary explanations have extremely high status, meaning they are often reflexively believed, even when they can't be backed up. It has become (has always been, really) a refuge for the kind of people who would rather make "bold statements" than work incrementally to increase our understanding. Wilson's statements sums it up all too accurately: make the statements now, leave to others to test it mathematically later.
On the contrary, social sciences have extremely LOW status, as your prejudicial comment sums up. Have you heard about the Cochrane collaboration, evidence-based medicine? You probably have. Why did it take so long to appear? Because medicine and molecular biology has high status, whereas the "social" population studies of epidemiologists had low status. So if the high-status people said, "from our understanding of molecular biology, this should work", for a long time that would be tried, even though from a 10.000 feet view it would have been obvious it did NOT work.
You need both kinds. You need people who take the bottom-up approach, building bricks of what we know, and assemble it into bigger things. Then you need to have people who take the top-down approach, because no matter how well the pieces fit, it's no good if the larger building can't actually stand. In some fields, like physics, these are closely intertwined. In others, they are tragically separated. For that to change, the white-coat status prejudices of people like you need to be broken down.
xkcd is not in the sudoers file. This incident will be reported.