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Government-funded research is instrumental to the development of corporate technologies: The NIH funded and is assessing the efficacy of Apple Watch’s ability to detect atrial fibrillation, a stroke warning sign, for example. However, the employee argued the private sector doesn’t have the same appetite for the slow-moving pace of publicly funded research.
“The lab does not create a product,” the employee said. “The lab creates an idea that can be transferred into a product in 20 years. No VC is gonna fund that. We don’t have output in that way, but we create something that the private industry should be happy to have.”
I likely, too, would have a hard time finding a job in industry if i were to be suddenly fired. It is so specific and specialised, where my age would pay against me due to lack of industry experience. It would require a lot of mental flexibility to adjust to the new reality. Not impossible, but quite challenging for sure. I do not envy them.
[...]
Fired NIH workers, many of whom have a doctoral degree and years of training in specialized fields, are either caught in an appeals process purgatory—petitioning to government watchdogs about the legality of their firings—or are confronted with the weight of the federal hiring freeze. Looking to the private sector for options, some workers aren’t convinced there’s a future there, either.
“I spent 20 years getting this job, and now I’m going to have to figure out how to do something else,” one fired NIH biologist, who wished to remain anonymous as she tries to get her position reinstated, told Fortune.
Are there numbers on how many manage to get their positions reinstated? That adds a lot of inefficiency too, the very thing we want to reduce, if in reality lots of firings turn out to be illegal.
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.
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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.
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The NIH has been corrupted by Fauci and Collins. Too many politicians not enough scientists.
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That's shocking, indeed. People really really suck at statistics.
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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
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Thanks for writing it all out. I probably would have contributed to the bad look this test gave~~
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
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)
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%
statistics or probability which are related but not the same thing imo
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Bayes Theorem
I can never remember the formula
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Nice comic!
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