31–40 of 105 posts
"It should be noted that the results cannot be estimated using a physician fixed effect due to a numeric overflow problem in Stata 15 which cannot be overcome without changing the assumptions of the logit model."
... The sad part was they didn't even choose a reasonable model in the first place.
This guy overstates his case somewhat. Consider: "If the original study says an intervention raises math scores by .5 standard deviations and the replication finds that the effect is .2 standard deviations (though still significant), that is considered a success that vindicates the original study!" Why the exclamation point here? The replication study isn't magically more accurate than the original study. If the orig…
> Stupidity: they can't tell which papers will replicate even though it's quite easy. I am not familiar with this work. What exactly makes a paper predictably replicatible?
I try not to look down on social science, for the most part data is data as long as you can reason about how it was collected and who by. The only thing that worries me a little (or a lot sometimes) is that there doesn't seem to be much "bone" for the meat to hang off of - that is, in physics, if your theory doesn't match experiment it's wrong whereas in social science you're never going to have a (mathematical) theo…
I try not to look down on social science, for the most part data is data as long as you can reason about how it was collected and who by. The only thing that worries me a little (or a lot sometimes) is that there doesn't seem to be much "bone" for the meat to hang off of - that is, in physics, if your theory doesn't match experiment it's wrong whereas in social science you're never going to have a (mathematical) theo…
If only it was all data, statistics and hypotheses in social science. As it stands, that's a relatively modern idea in the field. Take a look at some of the stuff they teach in sociology 101: https://en.m.wikipedia.org/wiki/Social_actions It's all utter fiction. The long drawn out ramblings of ancient white men who formulated theories as their favorite pastime. None of it is experimentally derived because there were…
It is taught in 101 because it is important when teaching a subject for the learner to be able to answer “And what again are we doing here?”
Earlier quoted context omitted.
If someone is selling you on rethinking an entire discipline they are probably overstating their case a bit. Or you’re ignoring them outright. That doesn’t mean they’re wrong, necessarily. Overcoming inertia is a huge challenge. Daunting, even.
Agree. In the face of institutional inertia - here crossing industry and academic fields - your starting point is when I see a fly I use a canon. It is extremely difficult to budge. As so the usual reminder: the first 51% of communication is repetition
Is this a famous saying? It sounds nice but I hadn't heard it before. (And Google doesn't pop up anything obvious.)
I try not to look down on social science, for the most part data is data as long as you can reason about how it was collected and who by. The only thing that worries me a little (or a lot sometimes) is that there doesn't seem to be much "bone" for the meat to hang off of - that is, in physics, if your theory doesn't match experiment it's wrong whereas in social science you're never going to have a (mathematical) theo…
I majored in Mathematics but out of curiosity I took some Psychology modules when I was in university. What I found baffling was their lack attention to details. They just seem to have an intuitive model of their subject and they were just reinforcing that intuition while overlooking any details that could have challenged it. Coming from a field where every symbol, punctuation matters, I realised to Psychologists exact details of a curve don't seem to matter much as long as the general trend made sense.
Someone who really impressed me was Dan Ariely who is a behavirol economist. Even though I didn't see any mathematics in his lectures, I loved his approach to the field. I'd be quite happy if more of social science took a similar approach even if they didn't back it up with rigorous mathematics.
Edit: from all this talk of reproducibility, I wonder what percentage of cutting edge ML research is reproducible (either from lack of public training sets / not enough compute)
This is practically required by reviewers and editors. If you wade into a topic area, you need to review the field and explain where you fit in, even though you know full well many of those key citations are garbage. You basically need to pay homage to the "ground breakers" who claimed that turf first, even if they did it via fraud. They got there first, got cited by others, and so are now the establishment you are operating under.
And making a negative reference to them is not a trivial alternative. For one thing, you need to be certain, not just deeply suspicious of the paper, which just adds work and taking a stand may bring a fight with reviewers that hurts you anyway.
Is there a similar study done on the physical sciences? I’m getting a bit of holier-than-thou feeling from this article. Edit: from all this talk of reproducibility, I wonder what percentage of cutting edge ML research is reproducible (either from lack of public training sets / not enough compute)
Other CS subfields that get a lot of criticism are "network science" and bioinformatics.