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It's smug comments like this - particular to engineers - which always give me a chance to chuckle about how little computer programmers actually understand scie
by KingMob 11y ago
It's smug comments like this - particular to engineers - which always give me a chance to chuckle about how little computer programmers actually understand scientific practice.
All the same problems happen in biological sciences, and are even worse in some cases, since there's more money at stake. Failures to replicate are rampant in medical/pharmaceutical studies.
If you want to claim biology's not scientific either, go ahead. But science is about seeking knowledge, not having total agreement all the time.
- 6stringmerc 11y agoWell I'm not wrong that the origin of psychology is simply a blending of philosophy and writing, which are fundamentally outside the scope of the scientific method. I went out of my way to argue that biology-based science is truly science (e.g. psychiatry) but that didn't seem to register. Oh well, audiences miss the point all the time.
- idm 11y agoResearch psychologists are at the forefront of science, the scientific method, and statistical methods. Haven't you ever wondered why all of these articles by psychologists have been questing the very fundamentals of the way science is practiced? Psychology, as a field, is our day's premiere example of a science that examines itself. The issues with reproducibility are being illuminated and confronted by - who else? - psychologists. You're wrong on virtually every count. The scientific method is the primary philosophy practiced by psychologists. I would encourage you to read more before making easily falsified statements like "psychology is ... fundamentally outside the scope of the scientific method."
- mattkrause 11y agoTo add some specifics to this pile-on: - The classical example of a randomized experiment is a perception/psychophysics experiment: the Lady Tasting Tea. This introduced the idea of a null hypothesis as well as Fisher's exact test. https://en.wikipedia.org/wiki/Lady_tasting_tea https://en.wikipedia.org/wiki/Lady_tasting_tea - Visual and auditory experiments more or less invented signal detection theory and used it to probe visual and auditory processing. Those ROC curves everyone loves predate "machine learning" by decades. - A lot of the fancy hierarchical and Bayesian modeling work have come from psychologists and social scientists (e.g., Andrew Gelman)
- entee 11y agoNevermind that perhaps the most prescribed class of psychiatric drugs has been called into question: http://blogs.scientificamerican.com/cross-check/are-antidepressants-just-placebos-with-side-effects/ http://blogs.scientificamerican.com/cross-check/are-antidepr... Psychiatry is a really, really hard field for canonical drug research. Mechanisms of disease are for the most part completely unknown, the mechanism of action for the drugs is often unclear in the patient (why stimulants treat ADHD? it's complicated...) and you've got the additional complication that diagnosis for the disease is purely symptomatic (in fact, that diagnosis process is nearly identical to that of psychology) so two people with the same symptoms may have radically different diseases, but hey, they'll both be in your clinical trial! Drawing a difference between Psychology and Psychiatry doesn't really hold up. Doing research on people is extremely difficult to get right, all medical and psychology research is subject to these problems.
- Retra 11y agoI guess Einstein wasn't doing science because he used thought experiments and philosophical arguments instead of _real_ experiments.
- bzbarsky 11y agoNothing wrong with thought experiments and philosophical arguments, as long as you use them properly. Proper use goes like this: 1) Identify some area where theory does not match experiment or other theories (in Einstein's case, there were known experimental issues involving the speed of light that were the basis for his work on special relativity, for example). 2) Use thought experiments, philosophical arguments, divine inspiration, inspired guesses, random guesses, etc to produce a theory that accounts for your known experimental data. I expect all of these have been used in the past, in fact. Einstein used thought experiments and philosophical arguments for special relativity but an inspired guess for the photoelectric effect. 3) Use your new theory to make new predictions about experiments that have not yet been performed. If there are no such predictions to be made from the theory that step 2 produced, then the utility of that theory is pretty limited, obviously. 4) Perform the experiments whose results you predicted in step 3 and see whether you predicted them correctly. It's important to publish the results of these experiments, especially if the theory did NOT predict the results you got. 5) Keep repeating steps 3 and 4 as much as possible, ideally with a variety of different predictions produced in step 3, to gain more confidence in the new theory. These are, of course, all "doing science". And insofar as they can be done by different people, one could spend all one's time on step 2 and be "doing science". But the scientific community as a whole is only "doing science" if it actually goes through all five steps. If it stops after step 2, that's not called "doing science".
- aidenn0 11y agoI think it is a problem endemic to all science that requires human experimentation. Human experimentation is extremely expensive in both time and money, and so often the power of experiments is quite low relative to the effect that one wishes to measure. Then, all of the original problems (expensive, ethical review board &ct.) still exist for those who might want to reproduce the effects, along with the fact that there is not only no incentive for researchers to reproduce an experiment, there are active disincentives.
- nsajko 11y agoIf the parent mindlessly bashed psychology that doesn't mean you too need to go generalize and label all engineers (programmers).
- KingMob 11y agoFair point. I consider myself both. I'm currently a programmer, but at one point, I was working on a neuroscience PhD, so I've been on both sides of this. The messiness of the data was part of the reason I personally left academia, but I saw people doing good work and trying to sift truth from mud, so these sorts of comments irk me.