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• jgps141010
In summary, the conversation discusses the potential relationship between stomach acid and heat expansion, and how it may affect the life span of athletes. The person asking the question is interested in whether probability theory can be applied to this field, and what methods are used. Probability theory is important for formulating hypotheses and analyzing data in statistics, which could be useful in this case. However, the complexity of the topic and the need to rule out random coincidence makes it a difficult task. Other techniques such as stochastic differential equations or Markov chain Monte Carlo methods may also be used to simulate and analyze the data. Additionally, the Arrhenius Law effects in Chemistry and the need for double-blind studies with co-factor correlation are also mentioned. Overall, the conversation

jgps141010

I am interested in stomach acid and heat expansion, for instance the stomach will become heated due to an athelete competing. The heat causes atheletes to live shorter than people who don't have their body heated so often. I do a lot of differential equations and number theory, but I was wondering if probability theory could help with this. What do you guys offer in this field that has to do with that?? The only thing I can think of with probability theory is that they have methods employed in their fields rather than something concrete like number theory..

It seems like you need some good solid data more than some math. Once you have the data then you should be able to analyze it with most any statistics package.

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jgps141010 said:
I am interested in stomach acid and heat expansion, for instance the stomach will become heated due to an athelete competing. The heat causes atheletes to live shorter than people who don't have their body heated so often. I do a lot of differential equations and number theory, but I was wondering if probability theory could help with this. What do you guys offer in this field that has to do with that?? The only thing I can think of with probability theory is that they have methods employed in their fields rather than something concrete like number theory..
Probability theory offers the theorems that are necessary for statistics which is important in your case. You formulate hypotheses in statistics, e.g. that frequent body heating shortens life spans in humans, and test them versus your data. Statistics gives you tools to decide how confident you can be in your hypothesis.

One note: What you are trying to do is quite ambitious. Life span depends on so many parameters, that it is extremely difficult to single out one of them. It also requires decades-long data. One of the most difficult parts with such setups is, that you have to rule out random coincidence. This is especially difficult in your hypothesis since sport also extends life spans by improving the cardiovascular system.

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Correlation with multifactor variables is done in Manufacturing Engineered controlled processes with a dozen variables that each affect the outcome using Taguchi Method calculations to determine sensitivity of each contributor. (e.g. wave solder process) But these are experiments where a few different processes are changed +/-10% at a time.

Your situation is different.( these are not my expertise) This could involve using techniques such as stochastic differential equations or Markov chain Monte Carlo methods to simulate the dynamics of acid levels in response to various stimuli. This could involve using techniques such as hypothesis testing, regression analysis, or Bayesian inference to draw conclusions from the data.

Then you have the Arrhenius Law effects in Chemistry that also apply to electronics but with different thermal limits of activation energy to failure. Would be live twice as long if we reduced our body temperature 10'C? No. But if your heart stopped below air temps < 0'C for 30 minutes, chances of brain damage are reduced after resuscitation ( from Dr in family in ICU).
Athletic hyperthermia is a real safety issue!

All double-blind studies must include a p probability factor for co-factor correlation, which is not my expertise, but this is a priori to add authentication value to any study.

https://www.wikiwand.com/en/Arrhenius_equation

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Not sure if the OP means measure-theoretic foundations of probability, which is of little use, or more practical implementation issues like this

https://en.wikipedia.org/wiki/Data_dredging

Which is either a list of proscriptions or a tool kit, depending on your predilection