I started thinking about this post a while ago because I wanted to talk about what the scientific method is like in reality as opposed to how it works according to textbooks and simple graphs like the one below (taken from here).
After looking around a little, I realized I could try to reinvent the wheel or I could start from what other people have said and go from there. I listened to a discussion on this book (Kuhn calls his over 100 page book an essay), and it sounded like the author has something interesting to say, though I will probably disagree enough that it will be interesting to write about my thoughts.
Kuhn starts off by talking about how the field of history has a role to play in upending the textbook story of how science progresses. According to Kuhn, textbook progression of science is just like the diagram above, and importantly the textbook progression also says that we are steadfastly marching towards the truth. Go Science! But, Kuhn says, that's not how science actually works. We often ignore data points as outliers. You may actually remember doing things like this in statistics class with box and whisker plots. Scientists also tend to get stuck on particular compelling theories, even when the data doesn't entire support them. Sometimes the holders of a previous theory have to die off before a new one is fully accepted. I can imagine many Newtonian physicists were too appalled by relativity to ever embrace it (hopefully covered in a later chapter). Lastly, Kuhn discusses how the order of discovery seems to affect which theories get adopted. He talks about how a new scientist entering a field with scientific skills but no knowledge of that field will likely arrive at a very different theory from another person doing the same thing, depending on which order experiments are done in.
Those are the phenomena that Kuhn is trying to explain. An interesting connection this sparked for me is that the rejection of outliers is also connected to why scientists don't often report negative results. If a project is going horribly, it may be because you aren't using your equipment right, or training an animal correctly, or any number of other completely scientifically boring reasons. When I meet with professors to discuss a work in progress, the first five or so iterations of the work are completely unpublishable because minor errors make the analysis (or the collected data) meaningless. Kuhn does raise an interesting point that properly identifying these sorts of errors is crucial to doing science well, and it is hard to tell (especially as an outsider) what should be categorized as an outlier and what should be categorized as an interesting anomaly. It is common practice for a scientist to tell another scientist that they must have done something wrong because the data "looks off". This is usually good advice.
Kuhn then goes on to introduce the idea of a scientific paradigm, which consists of a set of standards for what is normal, what amounts of error are acceptable in certain measurements, and what interpretations of data are valid. He says that normal science works basically as described in the diagram above, with the standards all in place. Here's a quote on it:
Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like. Much of the success of the enterprise derives from the community’s willingness to defend that assumption, if necessary at considerable cost. Normal science, for example, often suppresses fundamental novelties because they are necessarily subversive of its basic commitments.
He's quite persuasive in his writing, but I have to admit I disagree with his reasoning about why scientists behave this way. The suppression of fundamental novelties is because the scientist is acting entirely reasonably. Taking the motion of the planets as an example, a scientists who believed in Newtonian mechanics would be quite justified, having seen both the discovery of Neptune from the orbits of other planets, and the regular prediction of every planet's motion except Mercury. So being that scientist, do you really expect that you need to completely rethink everything you know about time and space? Maybe instead, there is something strange about Mercury that we haven't discovered. Can it be explained by strange composition? Maybe tidal forces with the sun? An invisible moon? Who knows? And furthermore, I regularly read about physics professors having crackpots tell them they solved every physics problem with some mystical-sounding theory (neuroscience has it's share too). If the new theory sounds like one of those, it's not surprising that the professor might take extraordinary persuasion to overturn Newton. At the very least, I hope Kuhn will discuss this as a very reasonable possibility, and if he does reject it I hope it is with good reason.
Kuhn wants to draw a distinction between Normal science as described above and times of Paradigm shift, when the concepts in use are changing, the standards shift, and a new paradigm emerges, under which we continue to do normal science again. I'm not sure the two are so separate, but maybe that is because the field of neuroscience is relatively young. In neuroscience we know we are unable to measure most of the variables that affect what we care to study, so new theories are practically expected, though if someone claimed magic rays caused our thoughts and it had nothing to do with neurons I would certainly be dismissive.
As a final caveat to all of this, I have heard (though I don't think he mentioned in this chapter) that Kuhn believes that the data itself changes when a paradigm shift occurs. Whatever he means by that, I hope that he doesn't mean that repeating a procedure after the shift will produce a different outcome. That way lies madness.
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