This is a great point about making sensible assumptions. Too often I see evidence that people think that data analysis should be devoid of assumptions, and any assumptions completely invalidate the analysis. In reality, almost all analysis will have some assumptions, and the good questions to ask is how plausible are the assumptions and what is the plausible impact of what happens if the assumptions are wrong.
Why should we expect there isn't a correlation? They do mention it in their analysis. At the end of the day, just because there might be a systematic bias in your result doesn't mean there is a systematic bias.
All real-world analysis (especially for observational studies) rests on certain assumptions. It is always true that these assumptions might be wrong, but it is important to think about whether or not the assumption is plausible. It seems plausible that on average, a lawyer who is able to get better outcomes when they don't settle is also able to get better outcomes when they do settle.
Furthermore, even if most cases are settled, the rare cases that do go to trial can have an outsized impact. Usually people settle because a bad judgment is devastating (as well as not wanting to pay legal costs).
It is always easy to criticize a data-driven analysis by saying its assumptions could be wrong. In the real world, all analysis is based on assumptions, some of which you can always claim might not be correct. But you have to really present an argument as to why and by how much the assumption is likely to be wrong, you can't just state that the assumption might be wrong. The assumption that cases which don't settle are not at all indicative of how well a lawyer performs is a very bold claim, much bolder in my mind (admittedly I am not a lawyer) than the claim that there should be some correlation between lawyer performance and results in cases that did not settle. It is possible that lower ranking lawyers settle less often, I'd like to see the data on that.
Furthermore, on average the effects you are mentioning will wash-out, unless there is a systematic bias whereby lower ranking firms and higher ranking firms settle in different manners.
The main thing I want from job descriptions is a salary range. The fact that companies don't post salaries is a strong counter-point to how companies complain about how hard it is to hire software engineers.
That is a pretty basic piece of info that companies like to exclude. They tend to just post "competitive salary" when in fact a lot of the ones that state that pay well below market.
But it is competitive. It's a competition to pay the least amount possible for the best employee possible. Hopefully a competition against other potential employers, not against the employee, but it's really a bit of both since salaries are a negotiated thing.
I think the post makes some good points. Fluff and buzzword skill lists are useful for entry-level positions, so that a potential candidate who wants to get into a new career and knows little about it has at least some idea of what to look up. But for higher-level positions, the things that candidates want to know about the company and the things that the company wants to know about the candidate, are a bit different, and they both know more about the industry/career path. So fluff and buzzword skill lists are as ineffective in a job description as in a resume.
I remember a company I had a contract with sometimes (not always) posted jobs and advertised a pretty high salary. These salaries were actually competitive and not just in line with the competition. You can't imagine how easy it is to find high quality people that way. Go figure...
In Norway, positions from public departments like the tax department usually includes salary range in the job description. They often pay very well too, while at the same time they have a very good description of what expertize they need. In fact most developers here in Norway should read those job descriptions, take notes and be ready fight for better salary for their next job interview.
The tax department here in Norway pays between 80k-130k annually for senior software developers. While earlier this year I got a 55k offer from a private company. I so wanted to show them the job listing from the tax department, but I just said thanks for the interview, but no thanks.
The hard thing about salary ranges is that there are multiple levels of an engineer, some of which can't always be factually expressed in terms of work experience or any other variable. Although, a great deal of companies claim to hire the best, in most cases, this is not the case. It's always a mixbag of "Ok-hires", "Brilliant-hires" and "Ok-hires-who-turned-to-be-brilliant".
Taking in account of all three, the salary ranges can be too broad. Yeah, Buffer and StackExchange are doing good but then again, quite a lot of times they have been said to be giving low salaries which also is compensated by the fact that they are super-remote-friendly.
I can't imagine if it'd work for every organisation though.
That does seem to be a very critical piece that is missing. Not that engineers say what they are selling themselves for in their applications either...
Why should they, though? When selling non-trivial product or service, and it's pretty safe to assume an engineer's time is not at all trivial, a good rule of thumb is to not reveal your price until the reasons to purchase are well understood and agreed upon.
What's worse in when they post a salary range but then go through the whole "what are your salary expectations" thing. Half the time the posted salary was all that interested me but I can't say that because I can't remember what it was listed as.
The problem is that the term data analyst has come to mean data reporter. Similarly "business analyst" generally involves tasks that are best solved in Excel. The "science" in data science is about testing predictions. But I agree data science is a terrible phrase.
You don't have to be productive all the time. It is important to have some time to relax and have fun. There are far worse things you could be doing than playing too many video games. You can try replacing video games with a more productive activity, but make sure it is something you enjoy doing and don't feel the need to quit gaming entirely.
I sometimes think of quiting cold turkey and find something else. This, however, scares the crap out of me, because I don't see myself replacing a 2 hour gaming session with a 2h book reading, for instance.
I guess I could start working on personal projects
> Taking papers at face value is really only a problem in science reporting and at (very) sub-par institutions/venues.
> WRT the former, science reporters often grossly misunderstand the paper anyways. All the good reproducible science in the world is of zero help if science reporters are going to bastardize the results beyond recognition anyways...
Science is funded by the public, and done for the public. Good science reporting is very important to ensure that science continues to get funded. Too often scientific papers are written in a way that makes them incomprehensible to anyone outside of the field, whether that is through pressure to use the least amount of words possible or use of technical jargon.
There's also the option that papers are written the way they are so that they still remain papers and not books. A five page paper on someone's findings is much easier to read than a 20-30 page paper where field specific knowledge is redefined and explained instead of referenced.
The explanation glosses over a few important details. Gradient boosting works by adding some small weight to the instances the model is incorrectly predicting. The amount of extra weight these instances get is a parameter that is tuned with validation - because this parameter can be 0, if you are doing correct cv gradient boosting trees is usually superior to random forests. You also do need to tune the number of trees you use in gradient boosting or else you will overfit.
Gradient boosting doesn't get nearly enough hype as compared to things like neural nets. The significant majority of winning solutions to Kaggle competitions for a non-image or text-processing dataset will use xgboost to do gradient boosting as part of the ensemble model. Furthermore, it is a really easy method to understand and use while still being state-of-the-art.
