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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So, as the #MegaMillions jackpot reaches a record $1.6B and #Powerball reaches $620M, here's my advice about how to spend the money in a way that will truly set you, your children and their kids up for life.

Ready?

Create a private foundation and give it all away. 1/

Let's stipulate first that lottery winners often have a hard time. Being publicly identified makes you a target for "friends" and "family" who want your money, as well as for non-family grifters and con men. 2/

The stress can be damaging, even deadly, and Uncle Sam takes his huge cut. Plus, having a big pool of disposable income can be irresistible to people not accustomed to managing wealth.
https://t.co/fiHsuJyZwz 3/

Meanwhile, the private foundation is as close as we come to Downton Abbey and the landed aristocracy in this country. It's a largely untaxed pot of money that grows significantly over time, and those who control them tend to entrench their own privileges and those of their kin. 4

Here's how it works for a big lotto winner:

1. Win the prize.
2. Announce that you are donating it to the YOUR NAME HERE Family Foundation.
3. Receive massive plaudits in the press. You will be a folk hero for this decision.
4. Appoint only trusted friends/family to board. 5/
Brief thread to debunk the repeated claims we hear about transmission not happening 'within school walls', infection in school children being 'a reflection of infection from the community', and 'primary school children less likely to get infected and contribute to transmission'.

I've heard a lot of scientists claim these three - including most recently the chief advisor to the CDC, where the claim that most transmission doesn't happen within the walls of schools. There is strong evidence to rebut this claim. Let's look at


Let's look at the trends of infection in different age groups in England first- as reported by the ONS. Being a random survey of infection in the community, this doesn't suffer from the biases of symptom-based testing, particularly important in children who are often asymptomatic

A few things to note:
1. The infection rates among primary & secondary school children closely follow school openings, closures & levels of attendance. E.g. We see a dip in infections following Oct half-term, followed by a rise after school reopening.


We see steep drops in both primary & secondary school groups after end of term (18th December), but these drops plateau out in primary school children, where attendance has been >20% after re-opening in January (by contrast with 2ndary schools where this is ~5%).

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