51–60 of 77 posts
I saw this headline and my first thought was that someone was claiming that a mind impacting virus that evolved in the ocean was causing scientists to do research with less ambition. Which is of course ridiculous lol. But a bug in a visualization library impacting science is also ridiculous.
Bizarre. How do people make such big, splashy findings that can mess with people’s sense of optimism about science and innovation, without doing the simplest types of checks on their data and methodology.
No, the question is: how did peer review not catch it? I have the impression that reviewers don't have the time or incentive to give papers more that a cursory review. Independent of this case, a great many papers are published where the only "proof" is a user study or survey with an extremely low number of participants, but it still gets published. Many papers don't publish their datasets and don't contain enough de…
Bizarre. How do people make such big, splashy findings that can mess with people’s sense of optimism about science and innovation, without doing the simplest types of checks on their data and methodology.
No, the question is: how did peer review not catch it? I have the impression that reviewers don't have the time or incentive to give papers more that a cursory review. Independent of this case, a great many papers are published where the only "proof" is a user study or survey with an extremely low number of participants, but it still gets published. Many papers don't publish their datasets and don't contain enough de…
* is the treatment of existing work semi-thorough (even experts don’t know everything) and fair?
* are the claims novel w.r.t the existing work? If not, provide a reference to someone who has already done it.
* can you understand the experiments?
* do the experiments and their results lead to the conclusions claimed as novel?
* does the writing inhibit understanding of the technical content?
No peer review I have ever seen or done would catch anything but the most egregious bug of this nature.
I saw this headline and my first thought was that someone was claiming that a mind impacting virus that evolved in the ocean was causing scientists to do research with less ambition. Which is of course ridiculous lol. But a bug in a visualization library impacting science is also ridiculous.
Earlier quoted context omitted.
> Charts killed the Challenger crew. https://www.tiktok.com/t/ZT8oG7ym6/ This is one of my favorite TikToks of all time, and you’ll see why. It goes into detail about how charts killed the Challenger crew. But the storytelling is second to none.
I composed this comment concurrently with yours, so I'm moving it here as a response... This is going off-topic, but Tufte's attempt to cast the problem as fundamentally one of poor data presentation is rather self-servingly tendentious, IMHO, in a way that unfairly attributes a degree of culpability to the engineers who tried to stop the launch. The excellent video you link to, taken as a whole, supports this view,…
Hypothetically, what would be the most fair argument in that situation? It’s quite remarkable that a line engineer was convinced the rocket was going to explode, even to the extent of hopping in his car with his daughter and frying to stop the launch after his company gave the go-ahead. Data presentation seems like one of the few things that could have convinced upper management that there was a serious problem.
One thing I don’t understand (possibly unrelated to your point): if there were very few launches in cold temperature in general, how could he have convinced himself that there was going to be a disaster due to the weather? If I were in his shoes, I might’ve talked myself out of it by saying "well, I suppose it’s true we don’t have much data about cold temperature launches; how certain am I that the cold weather problems till now weren’t a fluke or a non-issue?"
Damn hipsters should just use matplotlib like the rest of us.
Seaborn is a wrapper around matplotlib. It's popular because it removes a lot of the boilerplate from matplotlib and is pandas-aware
For example, you call the pairplot function with a dataframe, and you just get a matrix of correlation plots and histograms. Versus matplotlib where half the documentation/search results use imperative w/ global state and the other half is OOP, and all the extra subplots shenanigans you have to decipher to get something that looks good.
It's convenience, really. The people who use seaborn don't want to dive into matplotlib because the interface is kinda a mess with multiple incompatible ways to do things. It also documents what arguments mean instead of hiding most of them in **kwargs soup. You get plots in 1 minute of seaborn that would otherwise take 10 minutes in matplotlib to write.
Bizarre. How do people make such big, splashy findings that can mess with people’s sense of optimism about science and innovation, without doing the simplest types of checks on their data and methodology.
Trash comment. 1st. Splashy often comes from the media, not the scientists 2nd. One of the ways we discover problems with data is by plotting. When the plot library has a bug that hides a problem, well shit. 3rd. They did check their own findings multiple ways. Mistakes happen. The biggest critics of scientific mistakes are often those that have never done science themselves. Its easy, and its a cheap play.
Then I thought that it was a paper claiming that a bug in the seaborn plotting library in python was responsible for the decline in disruptiveness in science, which is absurd!
Finally I understood, that this is a paper that is debunking another meta paper that claimed that disruptiveness in science had declined. And this new, arxiv paper is showing that a bug in the seaborn plotting library is responsible for the mistake in the analysis that led to that widely publicized conclusion about declining disruptiveness in science. oh boy so many levels...
