We know how to test string theory but we lack the ability to generate sufficient energy to do so. We don't even know how to reliably test many of the theories generated by the social sciences. That's the difference between a robust science and a squishy one: effective methods for generating and testing hypotheses. String theory is part of the "generating" process. It makes falsifiable and (theoretically) testable predictions. There's no reason that experimental results are required for that process, so long as everyone is clear about the hypotheses not being experimentally supported yet. The separation between theoretical and experimental physics is particularly necessary because testing quantum physics hypotheses involves multi-billion dollar investments in particle colliders. Yet once that investment is made, the experiments are effective at testing the theories and the theories are effective at being tested by experiments. You can't say the same about the squishier end of the scientific spectrum.
"Generate hypothesis with CB and present experimental data that is mostly irrelevant to it" seems to be a good recipe for getting Nature papers, actually.
Supersymmetry and string theory have long been presented to the general public as "theoretical physics awaiting experimental validation." See, e.g., the Elegant Universe. Hell the fact that the LHC hasn't reported a new particle where most supersymmetry theorists expected one to be has prompted a rush towards moving goalposts to keep supersymmetry alive. That's not the sign of a robust theory.
You're conflating particle physics (which does need "multi-billion dollar investments") with quantum physics which sometimes only needs well designed table top experiments.
I would classify string theory as it stands as impressionistic mathematics.
In biology when you design a good experiment, run it, come up with a model that fits your experiment, no scientist would ever pretend that their articulated model was how things actually worked. It's close, it's reasonable, but many more publications would be needed to confirm it. A single publication just is not enough to decomplexify the entanglements in biology. Many other scientists will use that model as the basis for their newer experiment, and when their results do not comport, they alter, adjust, or if need be, discard the original model.
The trouble with biology is it might take an entire 'Cell' paper (12 pages of dense publication - ie, many many grad-student-years of work) just to come up with the most simplistic model for what the biological experiment observed. That doesn't mean it wasn't meaningful - it was just much more complicated and less easily well-describable than physics and chemistry experiments. But very much like physics and chemistry, there is a definite trend towards a well-described and predictable system. Models actually get increasingly accurate over time. And this feature might be part of the border in defining a 'robust' science.
Yeap. Bioinformatician here. Not only are most published models overly simplistic -- which everyone acknowledges -- but I think our bigger problem is we don't even have a reasonable "meta-model" of biology.
What I mean by that is, imagine experiments were instantaneous and free. How would we then incorporate the results into a mathematical/computational/predictive framework that describes biological reality in a way analogous to physics? AFAIK, we haven't the foggiest clue.
Biology is pretty theory-light and empiricism-heavy, probably because we have good results with things like "I dunno what this mold is secreting, but it seems to be killing the bacteria!"
So our experimental results are pretty precise (although arguably not "robust" between strains/conditions/etc), when people bother to use n>3, but the models and theory are anything but.
Things in physics can sometimes be knocked so far out of balance during an experiment it boggles the mind, while still rendering perfectly sensible observations.
E.g., I once heard the LHC described as "trying to determine how a grand piano works by throwing lots of them one after one down a very long set of well-defined stairs equipped with lots of very sensitive microphones and force sensors".
In other cases, nothing could be farther from the truth. Say you are measuring heat transfer coefficients in high-Re laminar flow through a tube. A single 100-micron step imperfection in the tube wall will render your results pretty much useless.
The distinguishing feature of physics is perhaps that one can know to a very high degree of precision before constructing the experiment which parameters/uncertainties it is very sensitive to.
What about the beautiful and often very precise linear relationship between radioactive dating of the fossil record, and genetic dating using the molecular clock?
What about the beatiful correspondence often found between the principle components of genetic variation and geographical position (isolation by distance)?
What about all the biochemical discoveries related to DNA function (including the existence of DNA itself), how mutations occur, about heritability?
What about everything we've discovered about genome composition and how it changes over time? (duplicate genes, pseudogenes, transposons, hotspots of various kinds).
Is a DNA sequence less precise than a spectral line?
> What about the beatiful correspondence often found between the principle components of genetic variation and geographical position (isolation by distance)?
Funny you mention this. There is a student I work with trying to observe this with metagenomics data with much less success than you might imagine.
By that measure physics isn't predictive either. Any moderately complex system and the best we can do is statistical models, often with little to no predictive power.
This is not currently possible at all in biology because even the most minimal functional, self-reproducing biological system is very complex. Indeed even a single protein is quite complex. I suppose by "complex" in this context I mean: lots of acting entities, and many physical laws operating at once rather than just a few.
Physics does have predictive problems when it is applied to weather, climate, etc, because those are complex systems. But that kind of the thing is a minority of the subject matter in physics.
There is a far greater number of humans working in applied physics than in characterizing isolated aspects of theoretical systems so I'd question how you judged "minority" there :)
Perhaps our disagreement is just in choice of words. The idea that "physics", and all that encompasses, is somehow more predictive than a subset of biology was what triggered my response. If instead you said we have excellent models for simple questions in particle physics, we may have agreed :)
As I mentioned on a sibling comment, a simple question like "how an organism will evolve" is of course enormously complex, and if we're going to evaluate the "squishiness" of our answers to it, it's better compared to our ability to predict specific storms a year in advance or how a protoplanetary disk will evolve into a specific configuration of planets. We don't cite those as squishy because we recognize the complexity of the systems involved (and the relative primitiveness of our models).
Using any definition for "physics" close to this, while the percentage of biological questions that involve complex systems is close to 100%, it is much lower in physics. The subsets of physics problems that do involve complex systems will suffer the same predictive problems.
In fact, this conversation has got me wondering whether "complex system" really means anything more than "a system whose behavior is hard to predict". I know that complex systems have other common attributes, but really the unpredictability seems to be the defining feature.
This is a long way of saying "I agree that we don't really disagree" :)
If we look at a (perfectly random) coin flip we can predict a 50% chance of heads. We can also predict the likelihood of distributions of values over x flips. If the system we are modeling is inherently statistical we would expect our prediction to be statistical.
You are also confusing the fact that the stuff in physics that isn't statistical in nature has extreme precision. Think of how well we know the orbits of planets.
Thus even our models of planetary orbits are statistical. The inverse-square law, GM1M2/r^2, even if it perfectly describes reality (probably, but not entirely certain! see [1]), will have some degree of measurement error in M1, M2, and r (not to mention G) and so the resulting Fg will be a distribution, not a single number technically speaking.
It seems that the situations where physics can best describe things with very high accuracy is when it can abstract away many relatively homogeneous particles or entities into a bigger "thing" with aggregate properties. For example, in fluid dynamics or gravity, you don't attempt to determine the behavior of individual particles, which would be subject to enormous uncertainty, only the behavior of the system-as-a-whole. By the law of large numbers then the uncertainties decrease dramatically.
[1] https://en.wikipedia.org/wiki/Modified_Newtonian_dynamics
And to your mention of everything being statistical because quantum, well there's a reason Newton's methods didn't require them to be powerful (useful or predictive). Because the likelihood of quantum like events happening on a macro scale is basically zero. Sure, your hand could quantum tunnel through a wall, but would we ever expect to see it within the lifetime of the universe?
We're talking about the relativity of wrong here[1]. Physics wouldn't have become so popular if it wasn't predictive. We don't need to be 100% to be predictive nor useful. Accuracy and predictiveness are two different things.
[1] http://chem.tufts.edu/AnswersInScience/RelativityofWrong.htm
No, I'm not. Or I didn't intend to, in fact I intended quite the opposite. I completely agree that "wrongness" is relative. "Wrongness" could be more accurately described as the amount of variance in a predictive model plus that model's divergence from reality.
My point was that all models and predictions are statistical/probabilistic, but not all have even the same order of magnitude of error. For shorthand, we pretend that models with very low variance/error are "exact" solutions, but in actual reality, they are not, they are just solutions that have a negligible error rate for the purpose at hand.
I am not implying anything like "well, psychology and physics both have probabilistic models, so they're equally valid". Their variance and error rate are very far apart. I agree physics is very predictive and has high accuracy but it is still probabilistic.
Definitely not. The models used in undergraduate physics classes, or even to high school physics are not statistical. A good example is ohm's law. When building circuits this is necessary to use. Works just great. Now this is different from any attempts at GUT, but that's a different ball game. And those are different models.
> For shorthand, we pretend that models with very low variance/error are "exact" solutions
Maybe the public, but not the actual scientists. For shorthand we generally say "is" instead of "to an error we can't measure" because it is easier to say. But if you read the research papers errors are always included. But that's just language. Doing otherwise would be pedantic. Yes, the public gets confused, but for all they are concerned with these predictions might as well be "exact". When the public starts venturing out of their realm without learning they get confused with other more important ideas like "observer" and "information". Don't get me started on how many people believe stupid quantum stuff.
> they are just solutions that have a negligible error rate for the purpose at hand.
This demonstrates that you understand my point too. Or that you don't understand what negligible is. But I think you understand. At a certain point we stop worrying. Why would you care if you could predict the location of a planet down to the 10^-40m? I get doing it just for fun and because you want to, but there is no practical purpose. Anything this accurate might as well be exact.
You are correct insofar as they are not presented as being statistical. But in reality, they are. Ohm's law is a good example. Resistors in reality do not have the exact resistance specified on the package, but rather are constructed within a certain tolerance, so that the final behavior of the circuit will be, again, a distribution. This would be an example of measurement error. The quantum effects also exist, as Intel will affirm as they are trying to build very small transistors, and the behavior of such transistors is probabilistic.
> Maybe the public, but not the actual scientists...
Ehh, I'm an "actual scientist". I work in bioinformatics & medical research. I don't care about what the public thinks for the purposes of this conversation. Even actual scientists will sometimes use this shorthand if the error is small enough, which is fine by me.
> At a certain point we stop worrying...but there is no practical purpose.
You're right. When we talk about the error rate in predicting planetary orbits, there is no practical purpose. My only point in my original reply was that the "exact" is a special case and a simplification of the statistical model, which is ubiquitous. If we are wanting to be technically correct, however, I stand by my assertion that all physical laws are inherently statistical.
I think we don't really disagree. This all started because you asserted there are phenomena which are "not statistical in nature", which I disagree with at a pedantic level.
I think we'll agree there. Because while you are technically correct you aren't practically.
Like how the Newtonian equations taught to undergrads literally don't have statistics. It isn't that it isn't presented to them that way, it is that they are using a different model. Going through physics (because this is the experience I have) you just keep learning better and better models.
As for Intel, you're confusing micro and macro scales. With the ohm's law you just measure the resistor before applying. This would be common procedure, depending on application. But this conversation is really arguing extremely fine points.
Sure, but that's not what I said.
The orbit of a single planet in isolation is extremely simple. Take the orbit and self-interaction of a protoplanetary disk around a star instead and you'll find that while our models can make some predictions, they will be able to tell you virtually nothing about the configuration of planets that will eventually form from them. We have weather models, which are actually better characterized than our models of planetary formation, but they will tell you nothing about where hurricanes will make landfall next hurricane season.
We can't make predictions about these things, but we don't call the models we do have "not really very predictive" because we recognize the extreme uncertainty in what we're asking in those cases. That was what I was responding to.
The idea that evolutionary theory is "squishy" because we can't figure out "how an organism will evolve" with all the monumental complexity hidden in that simple question is as silly as calling astrophysics "squishy" because it can't answer the above.