The Neuroscience of Screwing Up 190
resistant writes "As the evocative title from Wired magazine implies, Kevin Dunbar of the University of Toronto has taken an in-depth and fascinating look at scientific error, the scientists who cope with it, and sometimes transcend it to find new lines of inquiry. From the article: 'Dunbar came away from his in vivo studies with an unsettling insight: Science is a deeply frustrating pursuit. Although the researchers were mostly using established techniques, more than 50 percent of their data was unexpected. (In some labs, the figure exceeded 75 percent.) "The scientists had these elaborate theories about what was supposed to happen," Dunbar says. "But the results kept contradicting their theories. It wasn't uncommon for someone to spend a month on a project and then just discard all their data because the data didn't make sense."'"
Re:Ridiculous (Score:5, Insightful)
If your equipment is malfunctioning, you may end up with data that is fairly random where there should be some pattern or your measurements on your controls don't remotely match the values they should be. As an example, a standardized solution tests for a markedly different concentration than it should; a good sign that something is wrong. Things go wrong occasionally. That is why it is imperative that experiments be repeatable and have good experimental design.
Re:Why most scientists and engineers screw up (Score:4, Insightful)
Re:You never discard the data (Score:3, Insightful)
It's just a result of how science is performed. Science doesn't have low hanging fruits anymore, consequently any problem that someone investigates takes dedication, because it's intellectually hard or takes lots of effort or both. Most people aren't going to be motivated enough to put that much effort into it without already having an axe to grind, a point to prove, a pet theory to push into the limelight.
Also, in a lot of cases you don't know there is something interesting in the area you're looking at. I think what separates bad scientists from good scientists is how you realize when something doesn't match up to your preconceived notions and how you recover from conflicting data.
Or you can edit your data.... (Score:2, Insightful)
Is it just me or does this sound like an explanation for some of the Climategate science... But in that case they just massaged or ignored data that didn't agree with their conceptual framework of CO2 causing global warming.
Not that the skeptics are all that immune. They seem to cherry pick data almost as well (just not quite as successfully from the POV of selling their story to the media and political left ..)
Re:You never discard the data (Score:5, Insightful)
Re:You never discard the data (Score:5, Insightful)
This is a beautiful explanation of how science is supposed to work. In reality, science doesn't really work this way. It doesn't work this way in my experience as a scientist, and it doesn't work this way if you read the history of science.
For some good historical examples, see Microbe Hunters, by de Kruif (one of the best science books of all time, although you have to look past the racism in some places -- de Kruif was born in 1890). A good example from physics is the Millikan oil-drop experiment, where he threw out all the data that didn't fit what he was trying to prove -- but then claimed in his paper that he'd never thrown out any data. Galileo described lots of experiments as if he'd done them, even though he didn't actually do them, or they wouldn't have actually come out the way he described.
Michelson and Morley set out to prove the existence of the aether, published their results believing they must be wrong. Nobody else believed them, either. Various people then spent the next 30 years trying to fix the experiment by doing things like taking the apparatus up to the top of a mountain, or doing the experiment in a tent, so that the aether wouldn't be pulled along with the earth or the walls of a building. By the time Einstein published special relativity in 1905, most physicists had either never heard of the MM experiment, or considered it inconclusive.
When your results come out goofy, 99.9% of the time it's because you screwed up. You don't publish it, you go back and fix it. If every scientist published every result he didn't believe himself, the results would be disastrous. If you try over and over again to fix it, and you still fail, only then do you have to make a complicated judgment about whether to publish it or not.
The way science really works is not that scientists are disinterested. Scientists generally have extremely strong opinions that they set out to prove are true using experiments. The motivation is often that scientist A dislikes scientist B and wants to prove him wrong, or something similarly irrational, personal, or emotional. The reason this doesn't cause the downfall of science as an enterprise is that there are checks and balances built in. If A and B are enemies (and if you think the word "enemies" is too strong, you haven't spent much time around academics), and A publishes something, B may decide just to see if he can screw that sonofabitch A over by reproducing his work and finding something wrong with it. It's just like the adversarial system of justice. Society doesn't fall apart just because there are lawyers willing to represent nasty criminals. Einstein was famously asked what he would do if a certain experiment didn't come out consistent with relativity; his reply was that then the experiment would be wrong. Einstein fought against Bohr's quantum mechanics for decades. Bohr fought against Einstein's photons for decades. They were bitter rivals (and also good friends). It didn't matter that they were intensely prejudiced, and wrong 50% of the time; in the end, things sorted themselves out.
The problem... (maybe?) (Score:5, Insightful)
Re:You never discard the data (Score:2, Insightful)
Re:Or you can edit your data.... (Score:4, Insightful)
By "almost as well" I assume you mean "all the time". The "sceptic" arguments are nothing but a parade of cherry picking with little attempt at genuine investigation.
And there's no real evidence of the proper scientists massaging or ignoring anything. Just because a detailed, written account of everything doesn't exist in stolen, incomplete private documents doesn't mean it doesn't exist at all.
Re:Ridiculous (Score:2, Insightful)
Not religion, but federally funded dogma. More than 20 years ago I became aware of how dogma gets grounded in fundamental research: you need to write grants that fit the dogma. One hapless soul actually stood up during a big AIDS conference and suggested that the researchers were mere lemmings. He, of course, was shouted down, but he was only trying to tell the lemmings to keep an open mind. Fast forward 20+ years and the lemmings are still in control.
Our educational system is totally broken when the educated just want things to fit. Even in mathematics, we're promoting a crop of "just tell me what to do!"
Anonymous Coward (Score:2, Insightful)
As a researcher myself, I certainly hope they don't throw out data too often. There is occasion to do so...sometimes, when trying to establish correlations (admittedly the weakest form of describing a phenomenon, etc), you learn that there is not one. There are times you obtain data that simply says, "These two phenomenon do not strongly affect each other" or "Something we do not know about or have not accounted for is happening all over this mess."
This data could be kept forever in the unlikely event it will prove useful, especially if there is something else going on...could be as simple as a RF/EM noise (which actually happened to a coworker of mine, though I helped to figure out the issue and make alterations to block/filter this noise out.) In previous years, data storage was sometimes at a premium, although lately this is not an issue as HDD climb to extraordinary capacities (until that capacity becomes the norm, then it is merely ordinary.) My point is that rejected or discarded data, at least in my experience, is due to situations such as these.
Things such as "massaging" or ignoring data are not only horribly bad scientific practice, they are a tremendous drag on humanity's progress...you usually learn through failure, but we are led away from the truth by practices such as those.
Re:Throw away data? (Score:2, Insightful)
Proving a theory incorrect is often just as valuable as proving a theory correct.
I'd rather say proving a theory incorrect is just as valuable as proving a *hypothesis* correct. If it's a hypothesis, it's no fun proving it wrong (it wasn't established anyway, it might go against your intuition but nobody cares), and if it's a theory, it's no fun proving it right (what are you talking about, of course it's right, we already knew that).
I would elaborate on this but that would just be filler.
Re:Or you can edit your data.... (Score:3, Insightful)
Re:Ridiculous (Score:5, Insightful)
"It wasn't uncommon for someone to spend a month on a project and then just discard all their data because the data didn't make sense."
That doesn't mean the data is wrong, it means the /hypothesis/ was wrong, if not the theory, and needs to be modified.
If they're really throwing out date just because it 'doesn't make sense', they're doing religion, not science.
a) You've clearly never done any real research or you would be well aware of the hundreds of millions of ways you can screw up an experiment and get nonsense data ( bad machinery, you wired up a detector wrong, the cell lines you were feeding vitamin K happened to get contaminated by bacteria halfway through etc... )
b) There is almost never a clear difference between data and theory. The only raw data you have is a bunch of numbers on a piece of paper, in order to determine if they correspond to your theory or not you need to interpret the numbers somehow, and it may just as well be the interpretation that is wrong as is the theory you were trying to test using the interpreted data.
c) Because you are often restricted by cost and time it's often not feasible to do a full analysis of why your experiment did not work. Hence if you did not get any useful results ( uncertainty was too large, it seems obvious you must have messed up somewhere etc.. ) then frequently the only sane option is to conclude your experiment was a failure.
d) If scientists followed your advice we would never have got the electronic equipment you used to make your post.
Basically your ideas about what science is or should be are extremely naive and to anybody who has done even a high school chemistry experiment it should be clear you have no idea what you're talking about.
Bugs (Score:5, Insightful)
If the data doesn't fit your theory, the problem is most likely neither with the data (which is fine) nor with your theory (which may also be fine) but with the method you used to produce your data. You probably wired in an incorrect resistor, forgot to close a parenthesis in your Perl code, forgot to add the correct amount of EDTA to your reaction, etc. Then your results ended up looking like shit, and not surprisingly. Doing science is hard.
There's no need to postulate any grand conspiracies or take pot-shots at science in general. This paper is examining real people doing real shit. Most of the time we fuck up, and we're not smart enough to figure out where we made the error.
Seconded. (Score:2, Insightful)
Indeed. The sort of thing being discussed in TFA is one of the classic themes of late 20th century philosophy and history of science: the disconnect between traditional philosophy of science and the actual practice of science.
Kuhn's Structure of Scientific Revolutions is a good place to start. Just one tiny example of the book: Kuhn goes on about how during normal science, scientists perform experiments to confirm the results that they expect to get. When an experiment contradicts the theory, they don't automatically assume that the theory is wrong; on the other hand, they assume that the experiment was flawed.
Feyerabend and many other philosophers of science take a complementary stand to this by stressing the theory-ladenness of "facts." The claim that the "facts" contradict a hypothesis is never a theory-independent observation, but rather, the conclusion of a different theory that we may overthrow. Feyerabend's classic example is the Tower Argument that Aristotle used to refute the theory that the Earth moves. Wikipedia's article on Paul Feyerabend [wikipedia.org] has a decent, if terse, explanation of this:
Feyerabend goes on to argue that many of our most successful contemporary scientific theories (e.g., heliocentrism and geodynamicism) became so because their Renaissance and Enlightenment proponents held on to them and continued to elaborate on them despite them being contradicted by "the facts," as judged by the application of theories that were better established at the time (e.g., Aristotelian mechanics). That is, new scientific theories often succeed because their proponents keep working on them and improving them despite being contradicting by the "facts"; then as the new theories become stronger and better accepted, people start juding the "facts" by the lens of the new instead of the old, and forget the problems that the new theories were judged to have and never resolved (e.g., things like Newtonian physics not having the same explanatory range as Aristotelian physics).
Re:Seconded. (Score:3, Insightful)
Yep, they're totally right. For example, this 2003 paper [arxiv.org] claimed to have empirically verified the prediction of general relativity that gravitational forces propagate at the speed of light. The authors made some technical errors, which were rapidly pointed out by others in the field. The final answer is that actually nobody has the faintest clue how to test this specific, century-old prediction by Einstein. The reason is that nobody has figured out any alternative theory of gravity that (a) fits presently known experiments, and (b) predicts that gravitational forces propagate at some other velocity than the speed of light. There are other theories of gravity that satisfy a, and are inconsistent with general relativity, but they are all consistent with general relativity on b. Since there is no alternative theoretical framework, there is no way to design or analyze an experiment to test the question.
Re:Why most scientists and engineers screw up (Score:3, Insightful)
All fine and good, except the OP does not contain anything more intellectual than a bunch of bald assertions wrapped in the emotions of a xenophobe. In other words, you should have modded the GP informative, the OP is a well formed troll.
Re:Why most scientists and engineers screw up (Score:3, Insightful)
Are groups of people from very different locations, such as whites and blacks, different? Of course they are! Not so different as to be separate species (yet, and with global communication, maybe never), but evolved in different directions to adapt to different conditions. Just being different means there are activities for which one group will be better suited than the other group. There has to be, otherwise they aren't different, are they? Get over both the racism and the political correctness, and admit this basic fact. Skin color is a fairly superficial difference. Africa has by far the most diversity-- the Eastern African perhaps has less in common with the Western African or the Pygmy than with whites.
Much more harmful is the tendency to oversimplify fitness to a single, grossly over broad measure of a difficult to define concept that is not universally relevant, such as IQ, and declare one group superior to another based on only that. When a declaration of superiority is made, you may be sure it is for purposes of propaganda. Geniuses (defines as people with IQ > 140, or perhaps > 160) make their share of fatal mistakes, have flaws that can render their supposed advantages much less valuable, take risks and sometimes lose, sometimes let success go to their heads (most recently, Mike Leach), just like everyone else. Bobby Fischer was a genius, but that monomania which made him able to be World Chess Champion hurt him in so many other ways. The Soviet Union really bought into the idea of chess (and other contests such as the Olympics) as a good measure of a society's fitness, and devoted so much effort to it that except for Fischer, they pretty much mopped the boards with other nations' best players. And what was it all for? Propaganda that ultimately proved empty when Communism collapsed. I expect the Space Shuttle astronauts all do very well on IQ tests, but is that a smart gamble, risking their lives on that thing, for the fame and money they get? For other sorts of fitness, there are many fantastic athletes, rock stars, leaders, and the like who ended tragically. A small change in conditions can at a stroke reverse the fitness of most any trait.
Re:Ridiculous (Score:4, Insightful)
Your post is spot on. I'd mod up, but I wanted to clarify (I think you'd agree) that there's a difference between a successful experiment that is inconsistent with a theory and a failed experiment. The purpose of an experiment is not to prove a hypothesis, it's to TEST a hypothesis (or to gather data toward that end). Success means you make a useful statement that aids in the test. Failure means the data were not useful. It has nothing to do with the correctness of the theory or hypothesis.
In the specific quote mentioned, the data "not making sense" doesn't mean that they disagreed with what the experimenter was expecting, it means that they came back in a way that "couldn't happen." That is, that something had gone wrong making the experiment a failure. For example, in some tests I was doing a couple years ago with a prototype radio receiver, I needed to measure its noise level. As a signal, I would sweep a resistive load up and down in temperature---the load outputs noise with intensity that depends on its physical temperature. In this case, as a check, I would start with the load at a low temperature, then heat it past the point of interest, and then cool it back to the starting temperature. I would measure twice, once on the way up and once on the way down. What I found was that the results disagreed between the two measurements. That "does not make sense" in the sense of the article---the testing method was flawed.
In a sense, it was a successful test of a hypothesis. The hypothesis was that the receiver behaves in a particular way (which is what you'd consider the REAL hypothesis under test) AND that the test setup was a valid way to measure that. I disproved the joint hypothesis. In this case, it was the latter part that was invalid---the test was invalid---and I could say nothing about the receiver. This was simply a failed experiment. There is no religion going on by my not claiming that receivers don't behave as we think they do when I just discarded my results.
Every now and then, the reason for a failure might be interesting. This is rare, but when it happens can be responsible for amazing discoveries. In my case, it was a problem of thermal equilibrium. My devices were operating in a vacuum at very low temperatures (about 20 Kelvin) and it can be difficult to affix a heater or a thermometer to just the part of a device that you want to heat or measure....
The OP's statements mirror the general misunderstanding of the scientific method that is rampant in the non-scientific community. We need to help people understand this.
Because nothing works first time (Score:3, Insightful)
It's pretty rare for everything to go right.
I work with holography. I shine a laser at a piece of film, then develop the film. And presto, I get no image. Do I throw out the theory that exposing film to light should produce an image? No, I assume that I screwed up and go back and start again. It's not uncommon for me to spend 3 months of cleaning, aligning, measuring and so on until I produce a proper image. I then throw away all the "bad" data. Maybe, theoretically, that data could be useful, but there's too many parameters to account for.
Re:Ridiculous (Score:3, Insightful)
The real secret, Edison found, arguing it out with Charles Batchelor, was to raise the voltage to push a small amount of current through a thin wire to a high-resistance filament. It was an application of the law propounded in 1827 by the German physicist George Ohm, but it was still imperfectly understood. Edison himself said later, "At the time I experimented I did not understand Ohm's law. Moreover, I do not want to understand Ohm's law. It would stop me experimenting." This is Edison in his folksy genius mode. Understanding the relationship linking voltage, current, and resistance was crucial to the development of the incandescent lamp, and he understood it intuitively even if he did not express it in a mathematical formula.
Collisions with Outsiders (Score:3, Insightful)
Don't think the summary quite found the central point of TFA.
"Dunbar found that most new scientific ideas emerged from lab meetings, those weekly sessions in which people publicly present their data. Interestingly, the most important element of the lab meeting wasn't the presentation -- it was the debate that followed. Dunbar observed that the skeptical (and sometimes heated) questions asked during a group session frequently triggered breakthroughs, as the scientists were forced to reconsider data they'd previously ignored. The new theory was a product of spontaneous conversation, not solitude; a single bracing query was enough to turn scientists into temporary outsiders, able to look anew at their own work."
"I saw this happen all the time," Dunbar says. "A scientist would be trying to describe their approach, and they'd be getting a little defensive, and then they'd get this quizzical look on their face. It was like they'd finally understood what was important."
So that's it: The keys are multiple viewpoints, skepticism, and intellectual competitiveness.
The Neuroscience of Scientific Illiteracy (Score:5, Insightful)
I am calling this neuroscience because it has nothing to do with how the nervous system operates. In this sense I am following the lead of WIRED and/or Dunbar, who can't tell a neuro from a social. From TFA: "Kevin Dunbar is a researcher who studies how scientists study things". OK, he studies things called scientists. scientists are people. The study of people and how they behave is psychology. Science is a social activity. Investigations of social activities are sociology when taken as a whole, or social psychology when considered in terms of the activities of individuals operating within a social group. Dunbar studied social psychology, not neuroscience. There's not a speck of neuroscience cereal in it anywhere. There's very little if any actual social psychology, and psychology, or any science at all. There's talking about science, there's talking to scientists about doing science, and there's watching them do science. There's watching and talking about getting good results and not getting good results, and what people do in the matter case. If Dunbar thinks he's doing neuroscience, I suspect he's not even very clear on science itself, much less the various branches. And it does say he's "a researcher in", not that he's a scientist. I do research in curry recipes from different countries and cultures. I'm a researcher, but not a cultural curriology scientist.
In fact I'll go s far as to say he's a researcher because he knows precious little and is trying to find out basic things, not as is the case with most scientists, someone who knows a fair amount and is trying to build on that with new knowledge. He is apparently not clear on the difference between 'screwing up' and not getting good and/or clean results. This may well be because he was unclear himself as to what it was he was looking at and talking about, and he thought he was just not getting good or clean results, when actually, guess what?
He doesn't let loose any secrets. Anyone can talk to scientists and as what happens if and when things don't turn out as expected. If you get an honest (ie. less concerned with appearances than truth) scientist, anyone would get the same answers. Or one could simply read work from real social psychologists and others who study science and scientists and learn the same things. I myself always recommend Collin's & Pinch's "The Golem" as an illuminating, instructive and entertaining starting point.
And a technical point on methodology: a study that does not find a difference between groups, treatments, whatever, 'fails to reject the null hypothesis' (the assertion that there is no observable difference). It does not prove there is no difference, it merely fails to find one. It fails, but only to find a difference, not to produce a result. It can't say there is no difference, it can only say that it couldn't find one. And, it fails to find a difference, no matter how nicely or hapazardly the data come out. The only studies that "fail" produce no data. Scientists may further fail to find an interpretation, but there's no limitation on trying to figure this out, and it applies to both 'results' (reject null hypothesis) and 'no results' (fail to reject null). Studies that produce data that 'makes no sense' produce data that fails to reject the null. The 'making no sense' is a post hoc evaluation of the data based on an incomplete understanding of the design, collection, analysis or interpretation. Such evaluations are done in science, but they are not part of the scientific process. Therefore when this occurs, it is not a "scientific" result and cannot be taken to reflect in the nature or quality of the work done. If you can't figure what it means, you can't figure out. You cannot say that since you cannot figure it out, then you figure out that it fails. If you think you can take something that 'doesn't make sense' and then say that it makes sense in that it represents a failure, then you've contradicted the assertion that it makes no sense. All you can say is that you don't understand it, and since you d
Re:Why most scientists and engineers screw up (Score:3, Insightful)
Indeed!
No scheme of inequality can be defended as corresponding to natural fact.... Superior and inferior can be determined only with respect to a single quality for a single purpose. Nor can a man's qualities be added together and averaged to give a final score or merit. In short, men are incommensurable and must be deemed equal.
- Jacques Barzun
Re:Two Relevant Quotes (Score:3, Insightful)
"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."
Correct, as long as you take that to mean "experiments in general". It is possible to make mistakes in experiments, and you shouldn't throw out General Relativity because some lab newbie got the setup wrong.
As I said in my other comment [slashdot.org] on this issue, you have to decide which is more likely: that you got the experiment wrong, or the hypothesis is wrong, and this depends on your confidence in both. But you should never hide the result, or else you can get an informational cascade that leads to conformism to bad theories.