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Dec 10, 2021, 4:25:02 PM12/10/21

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Hi:

Is there literature discussing the issues about how statistical theories affect the construction or deconstruction of the hypotheses, theories, or laws in natural science, e.g., physics, chemistry, biology, and in engineering, e.g., electrical engineering, computer engineering, civil engineering?

For example, a famous/important hypothesis/theory/law could not be proved or disproved until someone use some statistical theories to resolve this issue.

Is there literature discussing the issues about how statistical theories affect the construction or deconstruction of the hypotheses, theories, or laws in natural science, e.g., physics, chemistry, biology, and in engineering, e.g., electrical engineering, computer engineering, civil engineering?

For example, a famous/important hypothesis/theory/law could not be proved or disproved until someone use some statistical theories to resolve this issue.

Dec 12, 2021, 3:37:38 PM12/12/21

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On Fri, 10 Dec 2021 13:25:00 -0800 (PST), Cosine <ase...@gmail.com>

wrote:

>Hi:

>

> Is there literature discussing the issues about how statistical

> theories affect the construction or deconstruction of the hypotheses,

> theories, or laws in natural science, e.g., physics, chemistry,

> biology, and in engineering, e.g., electrical engineering, computer

> engineering, civil engineering?

Well, I don't know what you mean by "statistical theories."

Are you asking whether "statisticians" are engaged in those

areas? There are surely a lot of high-powered mathematicians

in physics and chemistry these days. Sometimes the same

distributions have different names, depending on the specialty,

but some insights bleed over.

The interior of nuclear reactors and atom bombs are modeled

by the statistical distribution of the capture-cross-section. The

Cauchy distribution, also called the "Lorentzian distribution",

has no mean and infinite variance, so this model is

MRIs and CT scans are statistical methods of deconvolution.

I was asked (forty years ago) by a young physicist about how they

should consider sparse data that they had about the weight/power

of new nuclear particles -- different instruments have different

limits, and the measurements made near the max power were

prone to greater error. I might have added some to what he

already knew, since his focus had never been inference.

Those guys have caught up on the rules of inference. New particles

or phenomena,these days, are often identified after counting the

number of "events detected" over months or years, and if p < 1 in

10 thousand (or some such), Something New can be declared.

Astronomical events and studies also resemble (to me, at least)

methods in decovolution, ane they apply testing of significance.

What happens with a large number of gas molecules follows

the ideal gas law, for an ideal gas, PV= nRT . Applied statistics

give them the velocity of particles; exceptions to the law give

systematic departures. Statistical studies of those data lead to

further models.

>

> For example, a famous/important hypothesis/theory/law could not be

> proved or disproved until someone use some statistical theories to

> resolve this issue.

Astronomers and particle physicists are using statistical inference

to conclude that old theories are NOT sufficient, and the door

is opened to new suggestions. Sometimes there is a new idea that

some of them jump on. Our best, old theories are the ones that

have not yet been supplanted.

Statistics can show that some OLD idea is not enough; we can't

/prove/ that the new explanation has to be forever 'right.'

--

Rich Ulrich

wrote:

>Hi:

>

> Is there literature discussing the issues about how statistical

> theories affect the construction or deconstruction of the hypotheses,

> theories, or laws in natural science, e.g., physics, chemistry,

> biology, and in engineering, e.g., electrical engineering, computer

> engineering, civil engineering?

Are you asking whether "statisticians" are engaged in those

areas? There are surely a lot of high-powered mathematicians

in physics and chemistry these days. Sometimes the same

distributions have different names, depending on the specialty,

but some insights bleed over.

The interior of nuclear reactors and atom bombs are modeled

by the statistical distribution of the capture-cross-section. The

Cauchy distribution, also called the "Lorentzian distribution",

has no mean and infinite variance, so this model is

MRIs and CT scans are statistical methods of deconvolution.

I was asked (forty years ago) by a young physicist about how they

should consider sparse data that they had about the weight/power

of new nuclear particles -- different instruments have different

limits, and the measurements made near the max power were

prone to greater error. I might have added some to what he

already knew, since his focus had never been inference.

Those guys have caught up on the rules of inference. New particles

or phenomena,these days, are often identified after counting the

number of "events detected" over months or years, and if p < 1 in

10 thousand (or some such), Something New can be declared.

Astronomical events and studies also resemble (to me, at least)

methods in decovolution, ane they apply testing of significance.

What happens with a large number of gas molecules follows

the ideal gas law, for an ideal gas, PV= nRT . Applied statistics

give them the velocity of particles; exceptions to the law give

systematic departures. Statistical studies of those data lead to

further models.

>

> For example, a famous/important hypothesis/theory/law could not be

> proved or disproved until someone use some statistical theories to

> resolve this issue.

to conclude that old theories are NOT sufficient, and the door

is opened to new suggestions. Sometimes there is a new idea that

some of them jump on. Our best, old theories are the ones that

have not yet been supplanted.

Statistics can show that some OLD idea is not enough; we can't

/prove/ that the new explanation has to be forever 'right.'

--

Rich Ulrich

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