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The Neuroscience of Screwing Up
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."'"
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.
Sigs are too short to say anything truly profound so read the above post instead.
As other people have pointed out - sometimes the data is just crap due to the difficulty of making measurements. Sometimes you've measured something other than what you actually need to compare to theory, sometimes there's too much noise. The skill of a great experimentalist is being able to take good enough data that you can't justify ignoring it if it comes out different to what you expected.
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.
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I can't help but think that Neuroscience needs to calm down, sit back, and take a deep breath. We are examining a system and we are trying to reverse engineer it. We can't start out by trying to create elaborate hypothesis for large systems, we need to go low level and examine the simpler systems. I really think they should hold on to the higher cognitive models for a later time because we can't even completely model C. Elegans and it has the least neurons of any, current, living organism. The way I see it, I total expect their hypothesis to be wrong, because they don't thoroughly understand the low end of the system.
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"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.
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.
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
"I may be synthetic, but I'm not stupid." -- Bishop 341-B