Slashdot Mirror
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.
From that WSJ article: "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."
I don't really see anything wrong with telling people to still keep thinking about things, find out what they like to study, and get more math. More 'don't let current lack of math get you down' than 'you don't need math at all'.
Code or be coded.
Sociobiology is theories about how observed human behavior and social structures have arise from evolution. Where does cooperation come from? Where does homosexuality come from? How are these traits beneficial for animals and humans, and why haven't they been selected against? Sociobiologists come up with plausible and reasonable sounding theories for many of these, but most of them remain just guesswork if there isn't hard data and hard mathematical modeling (many remain just guesswork even with data and models). Wilson is right that you don't need to be proficient at math to succeed at science. But that's perhaps more a testament to the poor criteria by which some areas of science measure success than to what a scientist actually needs.
Are sensationalized bullshit. The original article did not make that claim, only that you shouldn't let a fear of maths or advanced maths prevent you from a career in the sciences. Obviously, don't plan a career in Physics, but there are plenty of interesting areas of study that don't require Calculus+ areas of math proficiency (sociobiology being one).
As an ECE, most of my studies were centered around differential equations. However, my sister, who did biology/chemistry(two hard sciences) with an intent to move on to dental school, hardly had to touch maths at all.
while(1) attack(People.Sandy);
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.
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.
Teams these days are really large, so much so that the data-collector is often not even the person designing the experiment. And that person is not the person doing the analysis of the data, who is not the person designing the mathematical model, who is in turn not the person implementing the simulation software. They all have to communicate in various ways, but they cannot each have all of those skills.
On smaller projects it may be the case that there's a more unified role of "experimental scientist", who does need to do all of understanding the model, designing the experiments, and carrying out the experiments. But on large teams the people actually collecting data need more technical skills, focused on operating various kinds of equipment properly. Someone else has drawn up exactly which experiments need to be run, but getting them run properly is not easy. Hence there are various scientific roles, like laboratory technician, that don't even require advanced degrees.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10
Have gnu, will travel.
Except when the math generates the insights.
For example, Dirac predicted the existence of anti-matter from a model of the electron with interactions with photons. For the model to work mathematically, he had to have a second particle, the positron which had opposite properties of the electron.
Then there's the search for missing planets. Neptune was found by noticing that Uranus didn't follow the orbit as predicted by the mathematical model of the then known Solar System.
Radioactive dating wouldn't be possible without a model of how decay works. That in turn has generated new insights.
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.
Comment removed based on user account deletion