Scientific Inference and How We Come to Know Stuff. Part 2 - Comments

In summary: It is quite common for incorrect ideas to give correct results. Indeed, it is very common for theories known incorrect to be used. Incorrect theories may be convenient and good enough for the purpose at hand, especially if they are much simpler than the correct theory.It is quite common for incorrect ideas to give correct results. Indeed, it is very common for theories known incorrect to be used. Incorrect theories may be convenient and good enough for the purpose at hand, especially if they are much simpler than the correct theory.This is definitely true! A theory that is correct may be much more complex than an incorrect theory.If they give correct results, why are the ideas
  • #1
bapowell
Science Advisor
Insights Author
2,243
260
bapowell submitted a new PF Insights post

Scientific Inference and How We Come to Know Stuff. II.

logicp2.png


Continue reading the Original PF Insights Post.
 
  • Like
Likes ProfuselyQuarky and micromass
Physics news on Phys.org
  • #2
A nice article!

One mistake I found:
Specifically, it provides the probability that we would observe the data O under the assumption that H0 is correct, p(O|H0)
That value would by tiny nearly everywhere (what is the probability to observe exactly 425435524 radioactive decays within some timespan, for any relevant hypothesis?). You need the probability to observe "O or something that deviates even more from the expectation based on H0" (which you used in the following text) or a ratio of probabilities for different hypotheses.
 
  • Like
Likes ProfuselyQuarky
  • #3
mfb said:
A nice article!

One mistake I found:
That value would by tiny nearly everywhere (what is the probability to observe exactly 425435524 radioactive decays within some timespan, for any relevant hypothesis?). You need the probability to observe "O or something that deviates even more from the expectation based on H0" (which you used in the following text) or a ratio of probabilities for different hypotheses.
Yeah, thanks. [itex]p(O|H_0)[/itex] is a distribution over [itex]O[/itex]. I'll reword that!
 
  • #4
In simplified form:

You think something interesting is happening. So you collect data.

Assume that nothing of interest is happening. How likely are you to have gotten such data in this case by pure chance? If it is unlikely, then that gives support to your idea that something interesting is happening.

If someone else gets the same result, then that is further support. And so on. If they get the same result by a different method, that is even better. This is called consilience.

It doesn't mean that your idea is correct, just that it is consistent with experiment. Not the same thing, but it DOES show that your idea has predictive power. In science that matters a lot. I'd say it is the main purpose of scientific theory: to predict what will happen in such-and-such a situation. Explaining why it happens is definitely secondary.

It is quite common for incorrect ideas to give correct results. Indeed, it is very common for theories known incorrect to be used. Incorrect theories may be convenient and good enough for the purpose at hand, especially if they are much simpler than the correct theory.
 
Last edited:
  • #5
Hornbein said:
It is quite common for incorrect ideas to give correct results.

If they give correct results, why are the ideas incorrect then?
 
  • Like
Likes Pepper Mint
  • #6
The problem with ''while it is impossible to verify a universal statement by observing singular instances, universal statements can be contradicted by individual observations'' is that what is a falsification is not well-defined. If a first year student falsifies Hooke's law through his experimental analysis nobody cares. And falsifications of statistical laws are uncertain by the means with which statistics is created and analyzed. Thus falsification has the same somewhat subjective status as verification: We can never be sure, once the laws are allowed to be imprecise in the slightest - which most modern physics laws are.
 
  • #7
micromass said:
If they give correct results, why are the ideas incorrect then?

Well, in some cases, we know that a model is incorrect (because it ignores relativity, for example), but it still makes predictions that are good enough for practical purposes.
 
  • #8
Just an observation: The type of "universal laws" that are always used in discussing induction or falsifiability almost never become part of a scientific theory. What I mean is that nobody just notices a correlation and proposes it as a universal law: "Hey! Every dachshund I've ever known has a name starting with the letter S. Maybe that's a universal law of physics!" When Newton proposed his law of universal gravitation, he didn't generalize from lots of measurements of the force between objects. Instead, his proposed law of gravitation was a model or hypothesis that allowed him to derive a whole bunch of other facts--namely, the shapes and periods of orbits of planets, as well as the fact that objects drop to the ground when released.

There are certainly cases where people just notice repeated patterns and propose a law that generalizes from those patterns. For example, the wavelengths of light emitted by hydrogen atoms was found to be given by something like [itex]\frac{1}{\lambda} \propto \frac{1}{m^2} - \frac{1}{n^2}[/itex] where [itex]m[/itex] and [itex]n[/itex] are integers, and [itex]n > m[/itex]. But it wasn't really taken as a "law of physics", but as a regularity that needed to be explained by physics (and the explanation turned out to be quantum mechanics).
 
  • #9
stevendaryl said:
Well, in some cases, we know that a model is incorrect (because it ignores relativity, for example), but it still makes predictions that are good enough for practical purposes.

Does that necessarily mean the model is incorrect? As far as I'm aware, there is no such thing as a perfect model that takes everything into account. And we all know GR is incomplete. So any model is incorrect then?
 
  • #10
micromass said:
Does that necessarily mean the model is incorrect? As far as I'm aware, there is no such thing as a perfect model that takes everything into account. And we all know GR is incomplete. So any model is incorrect then?

I would say that almost any model that we are currently using is not only wrong, but we know that it's wrong. But I suppose there is a distinction between being completely wrong, and being "in the right ballpark". For example, when we compute the energy levels of hydrogen using the nonrelativistic Schrodinger equation, we know the derivation isn't actually correct, because it leaves out relativity, and pair creation, and electron spin, and weak interactions, and GR and so forth. But we expect that taking into account those other things will make a small change, and won't completely overturn how we understand hydrogen atoms.
 
  • #11
stevendaryl said:
Just an observation: The type of "universal laws" that are always used in discussing induction or falsifiability almost never become part of a scientific theory. What I mean is that nobody just notices a correlation and proposes it as a universal law: "Hey! Every dachshund I've ever known has a name starting with the letter S. Maybe that's a universal law of physics!"
Agreed. This is why the discussion is focused on hypothesis testing rather than hypothesis formation.
 
  • #12
I found this article very informative. Keep going for good work
 

Related to Scientific Inference and How We Come to Know Stuff. Part 2 - Comments

1. What is scientific inference?

Scientific inference is the process of drawing conclusions or making predictions based on observations and evidence gathered through the scientific method. It involves using logical reasoning and critical thinking to form hypotheses and make conclusions about the natural world.

2. How do scientists make inferences?

Scientists make inferences by carefully collecting and analyzing data through experiments, observations, and other methods. They use this data to formulate hypotheses, which are then tested through further experimentation. The results of these experiments can then be used to make inferences and draw conclusions about the natural world.

3. What is the role of evidence in scientific inference?

Evidence is a crucial component of scientific inference as it provides support for or against a hypothesis. Scientists rely on evidence to make informed conclusions about the natural world, and the strength of their inferences depends on the quality and quantity of evidence available.

4. Can scientific inferences be proven?

No, scientific inferences cannot be proven beyond a doubt. In science, we can only gather evidence to support or refute a hypothesis, but we cannot definitively prove it to be true. New evidence or advances in technology may change our understanding and lead to different inferences in the future.

5. How do scientists ensure the validity of their inferences?

Scientists ensure the validity of their inferences by following the scientific method and using rigorous and unbiased methods in their research. They also carefully consider potential biases and sources of error in their data and results. Peer review and replication of experiments also help to validate scientific inferences.

Similar threads

  • General Discussion
Replies
32
Views
3K
Replies
9
Views
1K
  • General Discussion
Replies
6
Views
1K
  • General Discussion
Replies
15
Views
2K
  • General Discussion
Replies
15
Views
2K
  • General Discussion
Replies
6
Views
1K
  • General Discussion
Replies
12
Views
1K
  • General Discussion
Replies
7
Views
2K
  • General Discussion
Replies
4
Views
1K
  • General Discussion
Replies
24
Views
3K
Back
Top