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141 sats \ 15 replies \ @SimpleStacker 13 Mar \ on: Fired NIH workers fear bleak job prospects in the private sector science

I think as a physicist you are in a much better position, no? There is always a need for someone who can work with models and data. But, yes, the lack of private sector experience will hurt a bit, compared to others of similar skillsets. That's one of the reasons I've been so willing to take on part time private sector roles and contribute to FOSS.

258 sats \ 14 replies \ @south_korea_ln OP 13 Mar

I have been honing some marketable skills, so yes, it would not be impossible. But the skills that I am perfecting daily as a physicist do not contribute much to this marketability. What's marketable is my ability to solve problems, but that's something innate, and that hasn't been improved on much, other than for the very specific work I am doing.

Reminds me of this classic comic...

I mostly shared this article as it highlights not everyone who is fired seems to be a "career bureaucrat" who just checks in to get a pay without contributing to society. Or at least, not more than the way I am contributing to society, for things that may matter in decades from now.

I'd like to see non-partisan numbers.

If you're against the firings, it's easy to cherrypick human interest stories that show the firing is unfair.

If you're for the firings, it's easy to play with the numbers and say everything is more efficient now and less money is being wasted.

0 sats \ 12 replies \ @Bell_curve 13 Mar

The NIH has been corrupted by Fauci and Collins. Too many politicians not enough scientists.

https://boriquagato.substack.com/p/why-jay-is-the-right-guy-for-nih

40 sats \ 11 replies \ @south_korea_ln OP 13 Mar

That's shocking, indeed. People really really suck at statistics.

52 sats \ 8 replies \ @SimpleStacker 13 Mar

To be fair, the question didn't state its own assumptions very clearly. It didn't say that a person is randomly sampled from the population and given the test.

Typically, when a test is administered, it's because a patient has requested it, or come in with symptoms.

So to really answer the question, we'd need to know the rate at which people with the disease receive a test and the rate at which people without the disease receive a test.

Under real world scenarios, the real answer is probably closer to 95% than it is to whatever the "correct" answer is, which I guess was supposed to be:

 $\frac{\frac{1}{1000} \times 95\%}{\frac{999}{1000} \times 5\% + \frac{1}{1000} \times 95\%}$

However, the real answer is:

 $\frac{\frac{1}{1000} \times \alpha \times (1-FNR)}{\frac{999}{1000}\times \beta \times 5\% + \frac{1}{1000}\times \alpha \times (1-FNR)}$

where

\alpha

is the rate at which diseased people take the test,

\beta

is the rate at which non-diseased people take the test, and

FNR

is the false negative rate, which we also weren't told!

So... the badness at statistics goes all the way around it seems

40 sats \ 2 replies \ @south_korea_ln OP 13 Mar

Thanks for writing it all out. I probably would have contributed to the bad look this test gave~~

10 sats \ 1 reply \ @SimpleStacker 13 Mar

Statistics is genuinely hard, and I didn't realize all the nuances until I started working with real life data generated by real life people.

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0 sats \ 0 replies \ @Bell_curve 13 Mar

I'm too stupid to do it the correct way

my way would have been simple and wrong but...

false positive is 5 percent or 1/20

actual prevalence is 1/1000

20/1000 = .02 or 2 percent which is close to the actual answer, sort of

0 sats \ 3 replies \ @Bell_curve 13 Mar

one of the comments to the article (substack) addresses your question/assumptions

P(T|D) = probability of testing positive given the disease (sensitivity)

(I'll assume this is 100% since it wasn't specified)

https://open.substack.com/pub/boriquagato/p/why-jay-is-the-right-guy-for-nih?r=2slf6a&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=99767215

update: Step 3: Calculate each component:

P(¬D) = 1 - 0.001 = 0.999
P(T) = 1 × 0.001 + 0.05 × 0.999 = 0.001 + 0.04995 = 0.05095

Step 4: Calculate the final probability:

P(D|T) = (1 × 0.001) / 0.05095 ≈ 0.0196 or about 1.96%

10 sats \ 2 replies \ @SimpleStacker 13 Mar

yeah you need P(test|disease) to solve the problem.

The one that even fewer people appreciate is that you also need the false negative rate, which is not necessarily calculable from the false positive rate.