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People have no idea which sciences are robust

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Re: People have no idea which sciences are robust

#51

Post by xaa »

godelski wrote: post #48
xaa wrote: post #40

Earlier quoted context omitted.

Your overall point is correct, but I would add that there is no such thing as a non-statistical model or prediction in any science or aspect of physical reality IMO. For two reasons: A) reality is inherently statistical at the quantum level, and B) measurement error will always exist. Thus even our models of planetary orbits are statistical. The inverse-square law, GM1M2/r^2, even if it perfectly describes reality (p…

Yes, but you're implying "predictive" means 100% accurate. No science, no math, no language, will ever be 100% accurate. We say things have predictive power if we can, to a reasonable degree, if our results reflect our prediction. This is definitely true. And most those equations involve a pi. Pi doesn't have an end. There is ALWAYS and WILL ALWAYS be some uncertainty to our predictions. But is it that big of a deal…

> Yes, but you're implying "predictive" means 100% accurate.

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.

Re: People have no idea which sciences are robust

#52

Post by godelski »

xaa wrote: post #51
godelski wrote: post #48

Earlier quoted context omitted.

Yes, but you're implying "predictive" means 100% accurate. No science, no math, no language, will ever be 100% accurate. We say things have predictive power if we can, to a reasonable degree, if our results reflect our prediction. This is definitely true. And most those equations involve a pi. Pi doesn't have an end. There is ALWAYS and WILL ALWAYS be some uncertainty to our predictions. But is it that big of a deal…

> Yes, but you're implying "predictive" means 100% accurate. 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…

> My point was that all models and predictions are statistical/probabilistic, but not all have even the same order of magnitude of error.

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.

Re: People have no idea which sciences are robust

#53

Post by saycheese »

alganet wrote: post #16
saycheese wrote: post #6

Earlier quoted context omitted.

Okay, what is science?

I like the definition that Carl Sagan presented on Cosmos: > It is, so far, entirely a human invention, evolved by natural selection in the cerebral cortex for one simple reason: it works. It is not perfect. It can be misused. It is only a tool. But it is by far the best tool we have, self-correcting, ongoing, applicable to everything. It has two rules. First: there are no sacred truths; all assumptions must be criti…

Feels to me as if to you science means "Be truthful."

Am I missing something?

Re: People have no idea which sciences are robust

#54

Post by alganet »

saycheese wrote: post #53
alganet wrote: post #16

Earlier quoted context omitted.

I like the definition that Carl Sagan presented on Cosmos: > It is, so far, entirely a human invention, evolved by natural selection in the cerebral cortex for one simple reason: it works. It is not perfect. It can be misused. It is only a tool. But it is by far the best tool we have, self-correcting, ongoing, applicable to everything. It has two rules. First: there are no sacred truths; all assumptions must be criti…

Feels to me as if to you science means "Be truthful." Am I missing something?

It is less about truth, more about the process of trying to find it.

Re: People have no idea which sciences are robust

#55

Post by xaa »

godelski wrote: post #52
xaa wrote: post #51

Earlier quoted context omitted.

> Yes, but you're implying "predictive" means 100% accurate. 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…

> My point was that all models and predictions are statistical/probabilistic, but not all have even the same order of magnitude of error. 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 differe…

> The models used in undergraduate physics classes, or even to high school physics are not statistical.

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.

Re: People have no idea which sciences are robust

#56

Post by jcranmer »

stult wrote: post #42
jhbadger wrote: post #37

Earlier quoted context omitted.

On the other hand, those "squishy sciences" have nothing as squishy as string theory, which can't even be tested experimentally. In most sciences, theory papers without confirmatory experimental results to show that that it isn't mere fantasy are rejected.

There's nothing squishy about string theory. The line between currently untested hypotheses and those supported by reliable experimental results is quite clear in physics. You frequently see journalists representing the results of unreplicated garbage psychology experiments as scientific fact yet you never hear anyone claiming string theory is any such thing. We know how to test string theory but we lack the ability…

> You frequently see journalists representing the results of unreplicated garbage psychology experiments as scientific fact yet you never hear anyone claiming string theory is any such thing.

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.

Re: People have no idea which sciences are robust

#57

Post by godelski »

xaa wrote: post #55
godelski wrote: post #52

Earlier quoted context omitted.

> My point was that all models and predictions are statistical/probabilistic, but not all have even the same order of magnitude of error. 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 differe…

> The models used in undergraduate physics classes, or even to high school physics are not statistical. 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, aga…

> which I disagree with at a pedantic leve

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.

Re: People have no idea which sciences are robust

#58

Post by saycheese »

alganet wrote: post #54
saycheese wrote: post #53

Earlier quoted context omitted.

Feels to me as if to you science means "Be truthful." Am I missing something?

It is less about truth, more about the process of trying to find it.

Think we agree, just semantics at this point and me trying to simplify the expression. Thanks!

Re: People have no idea which sciences are robust

#59

Post by magicalist »

godelski wrote: post #34
magicalist wrote: post #30

Earlier quoted context omitted.

> but predicting how it will happen, i.e., how an organism will evolve, what genes will mutate etc, in a certain environment, is very difficult and really basically impossible. 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.

Statistical models doesn't mean that there is no predictive power. 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 natu…

> Statistical models doesn't mean that there is no predictive power

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.

Re: People have no idea which sciences are robust

#60

Post by magicalist »

xaa wrote: post #32
magicalist wrote: post #30

Earlier quoted context omitted.

> but predicting how it will happen, i.e., how an organism will evolve, what genes will mutate etc, in a certain environment, is very difficult and really basically impossible. 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.

I agree that the problem is complex systems, not biology per se. But physics is able to be quite predictive because it is able to isolate one basic phenomenon at a time and model it with great precision (gravity, electromagnetism, particle physics, etc). Then, if we want to build devices based on these phenomena from the ground up, we can also do that and predict their behavior with great accuracy (e.g., behavior of…

> 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).

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