2spicy_thrwaway's comments

If this new list of demands includes an “or else we’ll work in a last date” this thing is going to explode so much worse.

Question for those who think Stallman getting forced out is great news: There are people in this forum arguing that Stallman's statements weren't that bad. Should they be fired from their jobs?

Yay for throwaway accounts, I guess.

Sexism and racism in favor of a group which is usually hurt by sexism or racism. Or equivalently, sexism and racism against a group which usually benefits from sexism and racism.

And that pressure to get positive results is extra high right at the beginning of scientists careers, when they should be learning best practices! They do a big project for their doctorate and if it turns out poorly, they can be screwed. There's their chance to move on with their careers flying out the window. Maybe the already had their next gig lined up, but it's time to put that on hold :( Or, maybe if we just make an extra assumption, or twist and turn the data just this way, using this model and these covariates... Oh! Here's something significant! Here's your PhD welcome to science.

Even within the statistics community, there's a spectrum of quick-and-dirty vs fully rigorous. People with the ability and inclination to be fully rigorous often get treated as pedants and perfectionists (in the bad sense).

I often get that with business partners. "The data says <likely X, but with caveats/nuance/uncertainty/under certain assumptions we can't justify>" to which they respond "Can we just say X?" Or "can we get numbers on Y to support a presentation on Z?" when Y seems to support Z, but actually you can't draw that connection, so it's misleading.

Stuff like this happens because people treat extra rigor as pedantry and are comfortable making supporting assumptions that aren't supported by data. The people making fraudulent requests aren't aware that they're fraudulent (usually). In my experience, they just think they're being practical.

Here's another data point. Quantitative science B.S. from low-tier UC. Data analyst/engineer/data scientist. Silicon valley. Non FAANG.

Mix of promotions and job changes:

2009 $55k + worthless stock

2012 $80k + $10k or 15k signing

2013 $100k + $100k worthless stock

2014 $115k + $200k worthless stock

2015 $150k + $20k signing + $50-$100k stock

2017 $190k + $100k-$150k stock

2018 $260k + $200k-$300k stock

>Fewer engineers mean non-critical bugs in internal tools sometimes don't get fixed. We just don't have time.

This sounds like everywhere I've ever worked. I didn't even realize that the alternative actually existed!

No grad school, no papers, 7ish years work experience, 260k base + 100k-ish stock