Probability– teaching, bayes vs frequentists etc..

I see this kind of reasoning at the core of denouncing standard null hypothesis testing in financial models as this blog says

I see the core error being the same.i.e: trying to derive inferences from probability calculations that ignore conditional probabilities or treat them as no different from other probabilities.

Now, i have specifically tried to stay out of the Finance sector as a field of employment. I never really thought or questioned the whys’ of it, but am beginning to understand. I actually like money and am a reasonable saver, and like mathematics so the sector has been and perhaps still is a perennial attraction it does pay a hell of a lot more.
but am beginning to realize the reason i have instinctively flinched from it. the most available jobs are accounting and customer relations, i don’t have much stomach for the routine of accounting and am no good at customer relations.. but after that the jobs and openings are myriad higher and higher levels of abstraction
1.Quantitative Trading
2. derivatives trading
3. risk analysis
4. Portfolio management


Infact, i think this is the same problem with organizations doing normalizations of ratings and what not. I have a problem not because, i don’t think it makes sense to have all their employee ratings to fit to a normal curve, but i do have a problem in tweaking to fit exactly the normal curve at each reporting level. it’s just stupid and crazy application of standards and rules.

Also despite having a master’s degree, a bachelor’s in engineering, and having read a lot of science publications, and definitely having studied for exams, i never really understood the significance of p-values. I don’t really remember studying them very well, and somehow i don’t think they made sense if we studied it at any level of statistics course must look it up some other time.

(Obliquely related)
Probabability by stories:

I came across this story form of probability theory teaching.
See here

And was reading along, at the initial read of the story my first thought was that’s awfullay bayesian biased.
Soon realized, I never studied probability formally, definitely never beyond the dice/coin-toss example.
Have read, here and there(LW,NNT,EY and other blogs), knew there were three different interpretations,
but never was sure what those three were.

Anyway, reading the blog, it defines ‘classical’ as chalkboard situations, where we naively assume equal likelihood.
Now, that’s a category NNT would have called dangerously academic.(am somehow skeptical of this Defn.)

‘Empirical’ view relies on real-world frequencies.
(based on the examples, it’s more like projecting empirical observations from the past to the future)
Again, that sounds dangerously naive. Simply because it’s extrapolation with static/linear implicit assumptions.

‘Subjective’ view aims to express uncertainty in our minds, and therefore harder to define.

I am now finding all of these views rather, useless.
At this point am not sure what’s the point of these theoretical differences,
as they don’t seem to have a single effect on practice(i.e: reasoning with probabilities)

After reading the rest of the seriees, I get the reason why people are so divided on these interpretations.
But overall,think these should be personal preferences ultimately irrelevant to making a tight argument.(which should be based on the theorems)


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