The most frustrating aspect of working in computational probability/statistics is that it's basically impossible to construct algorithms that actually return well-defined probabilistic quantities and this results in no end of chaos.

At best algorithms can return approximations with quantifiable error, but to understand when approximations are useful you have to learn enough math to understand the exact result and how the algorithmic approximation relates to that exact result. Many do not do this.
More commonly programmers project the heuristics that prove successful in other computing problems -- pattern matching, type consistency, unit testing, relying on compiler errors, etc -- but these test only the algorithm and not the relevance of the algorithm to a stats problem.
Unaware of these subtleties many end up conceptually replacing the algorithm for the output being approximated, assuming that algorithmic properties are inherent and well-defined features of the underlying probabilistic/statistical system.
Needless to say this generalizes...poorly. Even worse: without formal knowledge of what is being approximated the poor generalization performance itself is easy to ignore and naive applications drift ever so steadily away from any well-defined mathematical objective.
At some point the algorithms drift too far and it becomes impossible to make any formal critique of the emergent heuristics. How can you criticize an algorithm when it's doing everything people believe it's supposed to be doing?
Of course statistical procedures and methodology in general follow the same pattern. Many methods that are abused today were at one point grounded in mathematical validation, only for those foundations to be gradually lost as the methods were taught less and less carefully.
All of this is to say that most math people going around critiquing heuristic methods and advocating for learning more irritating, burdensome math are not being exclusionary cynics: we're just trying to help ensure that contributions are, and will continue to be, constructive.
Probability and statistics is fundamentally difficult. Compromising the math by replacing subtle concepts with vaguely overlapping algorithms and heuristics leads only to superficial inclusion that typically does more harm than good.
In order to responsibly expand our communities we need recognize this challenge, respecting the math and consistently evaluating our current understandings while also working like hell to guide those starting their journey towards a meaningful destination.

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Patriotism is an interesting concept in that it’s excepted to mean something positive to all of us and certainly seen as a morally marketable trait that can fit into any definition you want for it.+


Tolstoy, found it both stupid and immoral. It is stupid because every patriot holds his own country to be the best, which obviously negates all other countries.+

It is immoral because it enjoins us to promote our country’s interests at the expense of all other countries, employing any means, including war. It is thus at odds with the most basic rule of morality, which tells us not to do to others what we would not want them to do to us+

My sincere belief is that patriotism of a personal nature, which does not impede on personal and physical liberties of any other, is not only welcome but perhaps somewhat needed.

But isn’t adherence to a more humane code of life much better than nationalistic patriotism?+

Göring said, “people can always be brought to the bidding of the leaders. That is easy. All you have to do is tell them they are being attacked, and denounce the peacemakers for lack of patriotism and exposing the country to danger. It works the same in any country.”+

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