Understand that science is a name given to both a method, and to a mostly self-consistent body of models that can be used to make useful predictions. Science doesn’t get things wrong. Science gets iterated upon.
Good science doesn’t get things wrong. Bad science gets things wrong all the time. No scientist is immune to implicit bias and implicit bias is frequently the cause of bad science.
I’ve taken to distinguishing between science(v), the method and science(n), the body of models and data. Science(v) is imperfect, but basically as close as we can get to objective truth. Science(n) can often stress conclusions further than their rigor justifies, but eventually regresses to the mean for the most part.
You can’t really question science(v) beyond its intrinsic epistemology, and no other method can really do any better. You can often question science(n), heck I can’t count the number of times “consensus” flip-flopped on red wine, coffee, fat, and so on. But eventually science(v) does bring science(n) to a stable empirical baseline.
There’s also the “science” that is your policy choices (personal or public policy) based on the science(n) and your values, risk tolerance, and lifestyle. Since the latter factors can change a lot over time, these policies can also fluctuate wildly and give the impression that “science” fluctuates wildly.
This is false. Godels incompleteness theorems only prove that there will be things that are unprovable in that body of models.
Good news, Newtons flaming laser sword says that if something can’t be proven, it isn’t worth thinking about.
Imagine I said, “we live in a simulation but it is so perfect that we’ll never be able to find evidence of it”
Can you prove my statement? No.
In fact no matter what proof you try to use I can just claim it is part of the simulation. All models will be incomplete because I can always say you can’t prove me wrong. But, because there is never any evidence, the fact we live in a simulation must never be relevant/required for the explanation of things going on inside our models.
Are models are “incomplete” already, but it doesn’t matter and it won’t because anything that has an effect can be measured/catalogued and addded to a model, and anything that doesn’t have an effect doesn’t matter.
TL;DR: Science as a body of models will never be able to prove/disprove every possible statement/hypothesis, but that does not mean it can’t prove/disprove every hypothesis/statement that actually matters.
Yes, and those are rare cases and so far apart from “corrolates with time” it is hard to impossible to know for sure when someone is outside that window.
I was also under the illusion that we’d done a lot of experiments trying to reelecrifiy frogs’ brains we have failed to get anywhere beyond muscle spasms off of the data and measurements we’ve been able to make.
Having things be unprovable in a body of models would make it not a 100% correct body of models. You know it’d be… incomplete. That’s what it means, we’ve mathematically proven you cannot prove everything that is true.
NFLS is about whether a particular claim is testable, and can therefore productively be debated (as in I’m not debating whether there is a teapot orbiting earth). The way you’ve attempted to combine these two ideas is odd.
Sorry, the point I was trying to make is that we will be able to know if any statement that is testable is correct.
I just wanted to clarify that your initial comment is only true when you are counting things that don’t actually matter in science. Anything that actually matters can be tested/proven which means that science can be 100% correct for anything that’s actually relevant.
Understand that science is a name given to both a method, and to a mostly self-consistent body of models that can be used to make useful predictions. Science doesn’t get things wrong. Science gets iterated upon.
Good science doesn’t get things wrong. Bad science gets things wrong all the time. No scientist is immune to implicit bias and implicit bias is frequently the cause of bad science.
eGFR estimation errors in African Americans is a prime example of that.
I’ve taken to distinguishing between science(v), the method and science(n), the body of models and data. Science(v) is imperfect, but basically as close as we can get to objective truth. Science(n) can often stress conclusions further than their rigor justifies, but eventually regresses to the mean for the most part.
You can’t really question science(v) beyond its intrinsic epistemology, and no other method can really do any better. You can often question science(n), heck I can’t count the number of times “consensus” flip-flopped on red wine, coffee, fat, and so on. But eventually science(v) does bring science(n) to a stable empirical baseline.
There’s also the “science” that is your policy choices (personal or public policy) based on the science(n) and your values, risk tolerance, and lifestyle. Since the latter factors can change a lot over time, these policies can also fluctuate wildly and give the impression that “science” fluctuates wildly.
It’s ironic to refute post-modern ideals with semantics.
And by Godel’s Incompleteness theorems, that body of models can never be 100% correct.
This is false. Godels incompleteness theorems only prove that there will be things that are unprovable in that body of models.
Good news, Newtons flaming laser sword says that if something can’t be proven, it isn’t worth thinking about.
Imagine I said, “we live in a simulation but it is so perfect that we’ll never be able to find evidence of it”
Can you prove my statement? No.
In fact no matter what proof you try to use I can just claim it is part of the simulation. All models will be incomplete because I can always say you can’t prove me wrong. But, because there is never any evidence, the fact we live in a simulation must never be relevant/required for the explanation of things going on inside our models.
Are models are “incomplete” already, but it doesn’t matter and it won’t because anything that has an effect can be measured/catalogued and addded to a model, and anything that doesn’t have an effect doesn’t matter.
TL;DR: Science as a body of models will never be able to prove/disprove every possible statement/hypothesis, but that does not mean it can’t prove/disprove every hypothesis/statement that actually matters.
We haven’t yet been able to ressurect anything by recreating vital signs in a corpse so there’s something we can’t measure or detect of life so far.
I’d argue that we can’t do a resurrection because that’s really complex, not because we don’t know how.
I’ll also point out that there are people alive today who were declared medically dead that live normal lives because we made their heart beat again.
The brain is a hard drive with only one working flash of the system
Yes, and those are rare cases and so far apart from “corrolates with time” it is hard to impossible to know for sure when someone is outside that window.
I was also under the illusion that we’d done a lot of experiments trying to reelecrifiy frogs’ brains we have failed to get anywhere beyond muscle spasms off of the data and measurements we’ve been able to make.
Having things be unprovable in a body of models would make it not a 100% correct body of models. You know it’d be… incomplete. That’s what it means, we’ve mathematically proven you cannot prove everything that is true.
NFLS is about whether a particular claim is testable, and can therefore productively be debated (as in I’m not debating whether there is a teapot orbiting earth). The way you’ve attempted to combine these two ideas is odd.
Sorry, the point I was trying to make is that we will be able to know if any statement that is testable is correct.
I just wanted to clarify that your initial comment is only true when you are counting things that don’t actually matter in science. Anything that actually matters can be tested/proven which means that science can be 100% correct for anything that’s actually relevant.