Terrible Advice From a Great Scientist 276
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."
He's right (Score:5, Insightful)
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.
Re:He's right (Score:4, Insightful)
Science doesn't work like that.
Re:He's right (Score:5, Informative)
Increasingly it does (minus the artsy chick, some fantasies never die). Very few current articles in biology have been written by one or two people. Even those articles have a long list of people that the researchers relied on for technical and intellectual support. It's not Charles Darwin walking down the road any more.
While there may be great insights developed by single 'intuitive' biologists, the intellectual foundations of those insights are going to come from thousands of disparate people. DNA chemistry and sequencing is an example here - how many biologists understand the chemistry of the analyzers? How many chemists understand the software?
I don't think H.O. is really correct though. At the complexity level that biologists are working at 'intuitive' thinking isn't going to help much. Working the numbers will.
I'd rather train a mathematician to be a biologist than the other way around.
Re:He's right (Score:4, Insightful)
I'd rather train a mathematician to be a biologist than the other way around.
With sarcastic apologies to Alex Belits, it doesn't work like that. I mean, it might, but there's issues of both interest and aptitude. Personally, I think you're both right. The best situation is to have both mathematics ability and some other kind of ability concentrated in a single human. Barring that, you can still get things done, perhaps just not as quickly. Thus, it is still preferable but not essential for everyone to have strong mathematics stills.
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Science and its requirements are dynamic, and nowhere is this more obvious than in the relationship between maths and biology.
When I was an undergraduate about 20 years ago, biology was the science you did if you liked science but didn't like maths. In the intervening years, largely thanks to the rise of bioinformatics, this is no longer true.
E.O. Wilson didn't need to have a mathematical background back in his day, but that day is now gone. We now have the technology to make quantifiable predictions, but t
Re:He's right (Score:5, Informative)
Re:He's right (Score:4, Interesting)
You are absolutely right, but the GP was making the point that the so-called "interdisciplinary" science is becoming the norm. Taking the author lists as an example was an unfortunate choice for an argument, but that doesn't invalidate his point.
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You make the assumption that a long list of authors indicates a truly collaborative research effort. In practice, this is very rarely the case. From my experience, nine times out of ten the work is done completely by the primary author or the first two authors. The rest of the authors are supervisors, technical managers, those who secured the funding, possibly a technician who assisted with the experiments, etc., who never even lay eyes on the paper until it's basically finished.
And from my experience, publishing dozens of peer-reviewed scientific articles, your experience is the exception. In fact, many sciences do not even utilize technicians. In the ten or so laboratories that I have worked in/with and the labs of the numerous professors that I talk with about their publication policies, exactly zero will allow someone authorship on a paper that they don't see until it's "basically finished." I'm sure some fall through the cracks, though certainly not the majority. However, I wo
Re:He's right (Score:4, Interesting)
And from my experience, publishing dozens of peer-reviewed scientific articles, your experience is the exception. In fact, many sciences do not even utilize technicians. In the ten or so laboratories that I have worked in/with and the labs of the numerous professors that I talk with about their publication policies, exactly zero will allow someone authorship on a paper that they don't see until it's "basically finished." I'm sure some fall through the cracks, though certainly not the majority. However, I would not generalize my experiences and neither should you.
My experience -- also publishing dozens of peer-reviewed scientific articles -- is quite different from yours and much more like that of the poster to whom you were responding. More than once I've found out that I was a co-author on an article when the publishing company contacted me to let me know that my article had been received for submission. That's even a step beyond what the first poster mentioned -- I didn't even see the article that I supposedly co-authored until after it was submitted for publication! I've also had my authorship credit manipulated so as to imply collaboration where there was none. It was accidental, I think, but afterward there was actually a story in the press about our non-existent collaboration.
Comment removed (Score:4, Insightful)
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DNA chemistry and sequencing is an example here - how many biologists understand the chemistry of the analyzers? How many chemists understand the software?
The answer is both most and not enough.
I don't think you could get through an undergraduate biology degree without being introduced to the basic chemistry underlying traditional (Sanger) sequencing, PCR, etc. Most chemists end up having to do at least some basic scripting if they're going to use automated analytical or synthesis equipment.
On the other hand, lots of biology papers have proven to be fatally flawed because of poor understanding/poor usage of statistics. Brain functional MRI studies, gene a
Re:He's right (Score:4, Informative)
This is true. About three quarters of the time, the paper is written by a single grad student, utilizing mostly his/her research along with a dabbling of other group members' work (and on occasion a more significant amount of work performed but often not understood by an undergrad), and then edited by the PI. Everyone gets their name on the paper, and often everyone is given a copy of the final draft to review before it goes to print just as a double-check, but the first author is typically the one that did all the writing.
Re:He's right (Score:4, Insightful)
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I can see it now. At a conference:
"And then we discovered our data fit this unusual mound-shaped pattern, which I thought was pretty neat..."
"Isn't that a Gaussian distribution?"
"Oh, so you've read our paper!"
...the day when biology becomes lessintimately connected to stats is the day when there are no more problems to explore. Perhaps when you're studying insect behaviour like E. O. Wilson you care more about intuitively-recognizable patterns, but the team's statistician should be a supplement moreso than
Re:He's right (Score:4, Insightful)
This would be funny if it were not true... Beware the biologists who tells you they found a number N of categories of each suspiciously smaller than the previous one by the same ratio. And never checked for fear that their result might not be publishable after all.
As in : "This a ground-breaking, paradigm shifting result: these identical individuals are not: some are short-lived, some long-lived, and we found an intermediate category, too" -- "oh, so your mortality curve follows an exponential law".
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don't get me started on MDs, they should never be allowed near a lab until they get a real university degree in a hard science. Which they should get _after_ their MD.
MD is a trade, like Carpenter or Mason or Lawyer. A hard one, which requires all sorts of qualities. But it does not qualify you to do science. Not remotely.
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The MD, PhD program is a popular way now of "legitimizing" an MD's role in science. In these programs, the PhD part of the degree is typically a joke involving no more actual research than than would be involved in an MS. They also get no more rigorous science classwork than they incidentally receive in the training for their MD. While they probably make great doctors, they don't seem to make very impressive scientists, and it's sad that they are seen as adequate replacements for properly trained scientists
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I don't know the MD/PhD folks that you have had experience with, but as a graduate student I find that the training of the MD/PhDs that I interact with on a daily basis is just as rigorous as that of the standard grad students.
No one has disputed that they are well trained. In their field.
But that field doesn't qualify them as scientists. Nor does it disqualify them. Medicine is perpendicular to science. Much of it is based on science. And it feeds science. But an MD is not automatically qualified to do science, unless she's a scientists too.
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Science doesn't work like that.
Presumably you have evidence to back this up, or is it something you know intuitively?
"literacy" is not "skill". (Score:5, Insightful)
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.
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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."
I'd argue that if "sufficient mathematical and comptuational capability to independently produce results for a research journal" includes such things as botching an Excel spreadsheet of a dataset that was lousy to begin with [slashdot.org], and in spite of this you still get published and win various awards and gold medals [princeton.edu], then the "not-so-low" standard isn't something to be proud of either.
Re:"literacy" is not "skill". (Score:4, Insightful)
But the math proved what they wanted to show, therefore it was "good enough"
Re:He's not right (Score:5, Insightful)
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.
Re:He's not right (Score:4, Interesting)
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.
Re:He's not right (Score:5, Insightful)
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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.
I couldn't disagree more. Ideally, the data collector should be completely out of the loop , and have no knowledge or bias about the hypothesis, or even the purpose of the experiment. This is what "double blind" experiments are supposed to achieve. The people that formulate the hypothesis, collect the data, and analyze the data should (ideally) be three different people, with different skill sets.
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Before gathering data, you've got to design an experiment. Without understanding the measurements and statistics involved, the experiment design might turn out to be crap.
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Without understanding the measurements and statistics involved, the experiment design will most certainly turn out to be crap.
Here, fixed that for ya.
Re:He's not right (Score:4, Insightful)
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.
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Again, the greatest value of math is not the math itself, but the ability to abstract, extract metastructures and isolate higher order patterns from what might otherwise be just chaos or noise. Agreed some, fields are more math intensive that others. Whereas studying primates in the wild (what few are left) mostly needs only the math to get your time and GPS values properly recorded, I would be a little more concerned for the folks a the Large Hadron Collider armed only with algebra. The same goes for most
Re:He's right (Score:4, Insightful)
Bullshit. Any scientist needs to understand basic maths, notably statistics. Not advanced calculus or complex algebra. But statistics and understanding what a model is is paramount. If you cannot recognised the patterns produced by common types of random processes, you may well start to believe you have found something.
And in fact just measured experimental noise.
Re:He's right (Score:4, Insightful)
True, but statistics usually requires intermediate algebra and could probably be taught in highschool without too much trouble. And the bottom line is that the formal theory is neither necessary nor sufficient for somebody to look at the data and see meaningful patterns. As long as you have somebody on the team that can whip up a model to fit the data that you can then test against future experiments, you're fine. There's no particular reason why any particular scientist needs that specialty.
And anyways, it's not just the statistics, it's the experience of having crunched many numbers and found many errors in the past. Realistically a specialist is much more likely to find the problem in an efficient manner than the other team members. Do enough math and eventually you can pretty much see the errors without even trying.
That being said, everybody really should have that class as part of their education as it's so helpful during the 99.9999% of your life where you don't have a statistician on hand to analyze your data for you.
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Amusingly, I have a mathematician friend who came up with an algorithm to solve numerically chemical problems. The things you describe are more the product of being a skilled technician than a scientist... As for the total synthesis of strychnine, I would think that doing that ab nihilo would require enormous amounts of maths. Or lots of trial and error.
People who do not understand maths fail to realise that mathematicians can frequently learn the essential bits of their specialty very fast, because they ar
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Amusingly, I have a mathematician friend who came up with an algorithm to solve numerically chemical problems. The things you describe are more the product of being a skilled technician than a scientist... As for the total synthesis of strychnine, I would think that doing that ab nihilo would require enormous amounts of maths. Or lots of trial and error.
I'm fairly confident that E.J. Corey [wikipedia.org] would not describe his Nobel prize-winning research as something that "a skilled technician" could do. And by your logic, my cell phone is a better mathematician than any human that has every lived. Your friend's algorithm is undoubtably cute, but would fail in practice the vast majority of the time.
In reality, it is impossible to perform a total synthesis in silico or really to do any chemistry ab initio because the subtleties are too complex to understand, let alone mo
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That said, I disagree with you in one thing. Although giving everyone the exact same knowledge is indeed a bad idea, requiring from every scie
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You have to balance it. In order for interdisciplinary teams to work you have to have familiarity with their specialty. Trying to work where you know nothing of their specialty just leads to problems like mistakes being made between fields.
Re:He's right (Score:5, Insightful)
Intuition and the part of math that involves being good at grinding through lengthy, dense calculations without making sign errors don't have to be the same person. However, a strong and intuitive sense of what math is capable of (which requires advanced mathematical education) do need to go together for scientific productivity. Otherwise, it's just like the techno-incompetent manager asking engineers to implement his "brilliant" physically impossible designs.
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Yeah, a team is important, even if it's only two people. James Watson described his working relationship with Francis Crick in "The Double Helix" - Crick was a polymath and was clearly the senior member of the pair, while Watson was brilliant but lazy (he described himself as "another uneducated Ph.D" whose mind was characterized by "an almost complete lack of chemical facts"). But they both apparently spoke a mile a minute and bounced ideas against each other, until Watson, with the benefit of seeing Ros
Re:He's right (Score:5, Insightful)
I know of certain articles in highly recognised journals which passed the review process, pushed by the editors who liked the message so much.
I also know that their data was largely noise, because the main authors clearly are math illiterate. Of course not everyone needs to be a mathematician, but every scientist should know the basics of statistics and be able to recognise a binomial or Poisson process after a cursory glance at the data.
Likewise not everyone should be some über-coder, but every scientist should be able to write small programmes in MATLAB, R, numpy, or whatever is appropriate for their field. These are basic qualifications which prevent you from churning out bullshit.
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MATLAB is inappropriate for any field.
It has niche applications to some small fields like science and engineering, but I wouldn't use it to balance the checkbook.
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Better MATLAB than wishful thinking... I'm not a fan personally, but I would rather people used that than nothing at all.
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Better MATLAB than wishful thinking... I'm not a fan personally, but I would rather people used that than nothing at all.
I'd like to introduce you to Julia [julialang.org]. The sooner it gets widespread, the better for mankind. Or at least for engineerkind...
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MATLAB is inappropriate for any field.
Why?
For calculations that involve lots of matrices, it's quite good.
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But is it as large as RMS Emacs?
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it's a shitty language. Still, in my opinion, much better that than nothing.
Why is is shitty, you ask? No objects, the syntax is not orthogonal (octave is a clone but seems to have done indices right, at least). Horrible, inconsistent libraries. Incredibly inefficient -- People going from naïve matlab to naïve c++ can get x1000 speed-ups.
And so on.
And yet, not coding at all is infinitely worse, so I don't give a hard time to my colleagues who at least try :)
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Re:He's right (Score:5, Insightful)
But it should be obvious that for some other problems this won't work. For example, it doesn't make sense to try and split the coding into a "creative coder" (who knows nothing about programming) and an "implementation coder" who turns the creative's ideas into actual code. The creative would toss out nonsensical ideas (like "instead of using vectors, why not use genetic algorithms?"), and then the implementer would have to explain why all those ideas are silly... or else they would just have to ignore the creative type and simply code something that makes sense.
In other words, generating good source code requires someone who knows enough about programming that they can see creative solutions. Their intuition is not separate from their programming talent: their intuition is based upon years of training and experience with source code, math, engineering, and so forth. That's where the good ideas come from.
Coming up with good scientific ideas is similar. Analysing scientific data even moreso. It's only once you have a deep, subliminal understanding of the important concepts that you're going to make substantive progress. Whether a deep understanding of math counts as an "important concept" depends on the field, of course... but I would argue that for science generally, the more mathematical know-how you have, the more informed and powerful your ideas will be.
Research != Programming (Score:2)
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"It involves lots of gnarly trig and vector math, which I am good at."
I'd argue that when someone says "math is important", more often than not is it not about trig functions, vectors, and integrals, but rather about logic, reasoning, and modern statistical inference.
Do what he did (Score:2)
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See also: Noam Chomsky on language
WSJ article title is somewhat misleading. (Score:5, Informative)
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'.
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Ssh, we're busy crafting a strawman here, and you're just trying to blow it down!
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Especially considering the number of students who are in the process of talking themselves out of careers in science because they think they aren't good at math. (Many of them are, but either have friends who have taken more math and belittle them, or lousy math teachers.) Yeesh. I think a lot of the problem is that we still have a culture where it's expected that most people won't ever manage much more than high school algebra... maybe calculus, and likely that by the skin of their teeth.
In my experience,
from the father of handwaving (Score:5, Informative)
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.
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If it has "socio" in it, it's bullshit. It doesn't matter who says it. It's "science" for people who don't know what real science is about.
The people learning to understand the physics of the brain don't have time to also learn about the esoteria of dead civilizations, at least not in detail. It takes both kinds of people to understand both what happened and why it happened.
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If it has "socio" in it, it's bullshit. It doesn't matter who says it. It's "science" for people who don't know what real science is about.
Maybe that's why the CDC hires so many sociologists to study disease transmission.
(Yup. Got one in the family.)
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And the evidence that these CDC sociologists are doing any good is... what?
Re:from the father of handwaving (Score:4, Insightful)
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.
Yes and no (Score:3)
Math is not necessary -- in fact it can be a serious liability -- in formulating hypotheses. For instance, much of sociobiology. On the other hand, it's indispensable for testing those hypotheses and sorting the valuable ideas from the attractive bullshit.
Which category holds much of sociobiology is a question beyond my own skills.
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Science without math (Score:2)
That's like literature without words...
Title and summary (Score:5, Informative)
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.
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True.
Doing original scientific research and merely learning about some of its results aren't quite the same thing. If they were, Scientific American would have already made me an astrophysicist, an economist and a neurologist.
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I realize that going into medicine doesn't make her a scientist, but the starting point for both paths is the same.
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There is far more to a useful general mathematics education than The Calculus
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I have never understood why Calculus is mandatory (4 semesters) for most everyone but statistics is not.
Probably (no pun intended) because one really needs (some) calculus to understand things like continuous probability distributions and the Central Limit Theorem.
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Exactly. "Statistics for people who don't understand math" courses are counterproductive and actually dangerous to the sciences. They churn out people who treat statistical analysis as a magical black box --- they've memorized which incantation sequences to type into some calculator or statistical analysis software package without the least understanding of what they're doing. The result is folks churning out research analysis applying all sorts of sophisticated-sounding statistical methods in inapplicable
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there are plenty of interesting areas of study that don't require Calculus+ areas of math proficiency (sociobiology being one).
Good luck passing the quals in sociology without a boatload of statistics that engineers never see, including formal design of experiments. Biology, at least according to the biologists I know, is much the same.
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Yes, but as I've already pointed out, the endpoint of her career is besides the point. She had just as many options of pursuing a scientific career, which is further than others get if they are discouraged from doing sciences by maths in the undergraduate level.
Fascinating insight (Score:5, Insightful)
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.
math comes second (Score:2, Insightful)
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Math may come second, but it does need to come. If Einstein had just been the guy who went around saying "dude, everything is like totally relative! Cosmic space-time bendy-warp, all-one time-cube, dude!," and expecting someone else to fill in the mathematical formalism, I doubt he'd be all that famous now. Einstein was able to write down his insights as tensor calculus equations --- that's why he's remembered as a famous scientist, not an incoherent ranting quack.
Re:math comes second (Score:4, Interesting)
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.
Not only that, but there are two mathematical (Score:2)
issues here. One is mathematical thinking—this is intuitive, and very difficult to teach; some people display aptitude for this (logical relationships, congruences, dependencies, correlations across qualitative cases, a "sense" for probability that is remarkably in tune with formal outcomes) and others struggle with it even if they become very proficient with Two, which is notation.
Too often, we conflate the former with the latter and call the whole package "math." But in fact, it is a deep, intuitive
Understanding statistics ... (Score:5, Funny)
Where Wilson is coming from (Score:2)
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Awful headline. (Score:2)
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The funny thing is the mere fact of calling him a 'great scientist' proves the blogger's point wrong, and the bulk of his post is dedicated to trying to explain away that contradiction.
Difference in worth of a degree IS math (Score:2)
The value of the degree in the marketplace tho is totally skewed towards mathematics. The more math you have to take to get your degree, the more money it is worth in the marketplace. Compare Computer Tech degree to Computer Science degree to Computer Engineering degree.
E.O. Wilson is perhaps technically correct about -needing- math early, but he is
Great, Angry blogger (Score:2)
The blogger is irate, angry, and irked. He lashes out with his words. Thank goodness we have bloggers in the world to be angry at great scientists.
There's no reas
I tend to agree with him (Score:2)
I think the problem is grade infl
It seems to me... (Score:2)
It seems to me that if one were to take this proposition seriously, it should appear as an article in a scientific journal, not the Wall Street Journal.
While I have no qualms with the Wall Street Journal, it does concern me when an article is published for a bunch of MBAs and CFOs that basically equates scientific research as nothing more than a bunch of individualized technicians. Research is not like web design where you have a design architect and a bunch of coders. But the article, phrased as it is, m
"OK to stop after Calculus" != "no math" (Score:3)
Extremely crap 'scientist' dribbles rubbish (Score:2, Informative)
America is the land of pseudo-science, and pseudo-scientists who push the 'right' agendas can easily rise to the top of their profession, and be lavished with all kinds of prizes and recognition.
-The depraved monsters who created and executed the 'scientific' studies to inject healthy black Americans with syphilis, and watch them suffer untreated, were highly regarded doctors.
-The depraved monster who photographed generations of young men and women naked at ivy-league universities all across America in orde
Everyone is talking past each other, again (Score:2)
Wilson states that to do good science and to be a good scientist you don't need to be a math wiz. Iddo states to be employable in the tech and science field the more math the better. Am I the only one who has noticed these aren't the same point? Iddo is worrying that if your C.V. doesn't show enough math you won't get the position to do the science at all. Wilson says you can find a place for yourself that uses the math you already know. Wilson is optimistic, Iddo is realistic/pessimistic. Wilson succeeded
Branches (Score:2)
Biology has an advantage over many other sciences. You can apply close study to the subject. But when one crosses over into fields like physics there are situations where the degree of resolution has reached its end and now mathematics becomes almost the singular tool. The ability to use various unusual logics and to reduce the question into equations is perhaps as far as we can ever hope to go. Trying to approach subjects like the underlying fabric that supports the universe or much of qua
Biologists don't know research? (Score:2)
I keep hearing this over and over from people. It seems like biologists are never educated in certain aspects of basic research. I know a CS grad student (who also has a degree in biology) who was talking about Google Scholar with some other biology grad students; they started taking notes because they'd never heard of it before. I asked how they managed to cite anything, and I was told that they do get the journals as they are published, and they do read, but they never SEARCH for anything.
Science is prediction, not explanation (Score:2)
Wilson is (or was) an observational biologist and naturalist. His book "The Ants" is great, but it's a picture book with essays.
For a science to lead to applications, it must have predictive ability. The hard line on this is from Sir Fred Hoyle: "Science is prediction, not explanation". Much of engineering is about prediction - being able to figure out what will work before you make it. Without that, you can't build anything big or complicated and get it to work.
The market for scientists in fields with
Faraday's an example (Score:3)
"Faraday was an excellent experimentalist who conveyed his ideas in clear and simple language; his mathematical abilities, however, did not extend as far as trigonometry or any but the simplest algebra."
http://en.wikipedia.org/wiki/Michael_Faraday [wikipedia.org]
Sociobiology (Score:3)
This is not a science.
What E.O. Wilson Wrote is 100% Correct (Score:3)
Did anyone actually read Wilson's article... including the irate, myopic blogger who is projecting his own bias while criticizing Wilson for the same?
Well, I have a professional secret to share: Many of the most successful scientists in the world today are mathematically no more than semiliterate.
In my fifteen-or-so years as an academic scientist, I have found this observation to be 100% correct and I have worked with some incredibly famous and well-respected scientists not unlike E.O. Wilson.
Far more important throughout the rest of science is the ability to form concepts, during which the researcher conjures images and processes by intuition.
In other words, math skills have nothing to do with creativity and science is driven, at its most fundamental level, by creative thinking.
Pioneers in science only rarely make discoveries by extracting ideas from pure mathematics. Most of the stereotypical photographs of scientists studying rows of equations on a blackboard are instructors explaining discoveries already made.
Math is a descriptive language, not an engine for discovery, duh.
Ideas in science emerge most readily when some part of the world is studied for its own sake. They follow from thorough, well-organized knowledge of all that is known or can be imagined of real entities and processes within that fragment of existence. When something new is encountered, the follow-up steps usually require mathematical and statistical methods to move the analysis forward. If that step proves too technically difficult for the person who made the discovery, a mathematician or statistician can be added as a collaborator.
Modern science is too complex for one generalist to do everything (and to take credit for it). These days everyone is a specialist, with a PhD in a very specific subject, and they all work together to bring ideas through to discoveries and eventually to technology. Would anyone argue that the POTUS runs the entire federal government by himself, being a world-class expert in everything from speech writing to foreign policy? Then why is it so hard to imagine that great discoveries are supported by the collaborative efforts of many, with one generally receiving the lion's share of the credit for the actual discovery?
The response of the blogger focuses on the idea that Wilson is an outlier and that, like Bill Gates dropping out of college, his resume should not be used as a template. But Wilson is not arguing that he was successful because he was semi-literate at math, he is arguing that you can be successful by focusing on what your good at, and complimenting your abilities with fruitful collaboration. His reason for making this argument is simple; too many people that would otherwise make talented scientists shy away from the sciences because they aren't good at math.
My two cents: there are, very broadly speaking, two principle kinds of scientists (with many exceptions). There the creative types, who are rarely good with math, often lack attention to detail, but who are astonishingly good at creative problem solving. Then there are the analytic types, who are too skeptical to be creative, are often detail-oriented, but who are astonishingly good at analyzing and understanding raw data. The best science is performed by teams comprising both types of people who respect and trust one another.
Re: (Score:2)
Why would you believe what E.O. Wilson says? Sociobiology is crap like this: "People do X. That's because evolution makes people who do X more likely to reproduce."
Essentially it gives the same explanation for every observation, without ever making any testable predictions. People like it, because it means that they don't need to take responsibility for how they act: after all, the great scientist says that evolution has selected for people who do that.
Re: (Score:2)
I would say it's actually less likely today that you will be able to "rapidly alternate between experiment and quantitative analysis" even if you want to. Roles are much more specialized, and labs much larger, than they used to be.
Re: (Score:2)
Sure, you can learn it, and probably should. But most large-lab workflows aren't set up for the same person doing the wetlab work to also be doing the data analysis, even if they want to. Their job is to stay in the lab and get more data.
Re: (Score:2)
so important scientific principles cannot be taught in grade school? nonsense, the scientific method ( not the only one science uses) itself can be expressed non-mathematically.
I've worked with guys like that (Score:2)
They go into the lab and discover circuits.
I have to admit, though, that I've never run into one who discovered a good delta-sigma analog-to-digital converter. Or anything else more than trivially complex.