1. Whether to summarize data by a handful of summary statistics or a full density. Obviously, some statistics reported in isolation can misrepresent the underlying distribution, but these considerations ultimately depend on what specific point one seeks to make with a plot. There's no reason a priori that visually annotating summaries/quantiles on a distribution plot can't be helpful (quite the contrary).

2. Whether to "smooth the data" (read: perform kernel density estimation). In some sense this is a long-solved problem: there are mathematically grounded methods for estimating the optimal KDE bandwidth (with varying degrees of assumption on the underlying distribution), which are what's used by any serious plotting library. And whether authors adequately describe what they're actually plotting is a separate matter. That said, there are many reasons not to show a KDE over, say, a binned histogram, especially with raw data and/pr small sample sizes, but these are entirely orthogonal to the choice of displaying a KDE as a violin plot versus something else.

3. How to normalize densities. With raw data, you probably want to compare frequencies (a proper pdf). If displaying just a single distribution, there's obviously no reason not to show the density (it's only a trivial rescaling of the axis tick values). When comparing multiple, the decision again depends on the point of the plot. Losing dynamic range for broader densities when compared with narrower ones can be counterproductive. E.g., in Bayesian parameter inference (where the data are MCMC samples), we almost never compute the actual normalization factor, but rather want to compare relative probabilities (i.e., within a single distribution) of different parameter values across different distributions. Of course, nothing forces one normalization over another for violin plots.

All of those are separate (and rectifiable!) issues from the defining characteristics of a violin plot:

1. Distributions displayed vertically rather than horizontally (both being harder to interpret and inappropriately suggestive). We almost exclusively visualize functions plotted vertically across a horizontal coordinate. I think this is the only valid, specific criticism of the common violin style itself, but the fix is of course trivial.

2. Horizontal violin are then only different from a ridge plot by a) not overlapping (which to me is a major improvement over standard ridge plots, but also trivially fixed) and b) being displayed symmetrically. I find it slightly easier to compare relative heights in the symmetric version, especially when comparing many distributions (such that each is relatively narrow). Even if not, the difference is so superficial/trivial that I don't find it worth arguing about.

Beyond this, the video's main argument (repeated every minute) seems to be that "it's bad, it's just bad", but there are only so many ways to make a 5 minute argument fill a 42 minute video. (This style of video is so grating to me.)

The black body distribution of the CMB is the (confirmed, of course) prediction of the Big Bang. The structure, age, etc. all depend on the cosmological model, and the claims that no such model can explain observations is ridiculous, given the counterexample of the \Lambda CDM model, the cornerstone of the field for decades now, that explains them all.

It's almost impressive how obstinately you've convinced yourself of something so blatantly wrong and out of date, using only a reference predating the entire modern era of cosmology that you even admitted to not having read "for a while." A far, far cry from engaging seriously in a topic.

Like with the frontier LLMs, seeing commentary on this site on topics that I'm an expert in makes me seriously doubt whether I should lend any credence at all to what's said about those that I'm not.

Perhaps part of the reason for the bad takes in this thread is due to taking "language" overly literally (perhaps also the fault of the linked blog post itself). I think one thesis of the above tooling is that, when tuning and generating code (CUDA, OpenCL, what have you) at runtime, the best "languages" for these abstractions are, amusingly, scripting languages like Python. Having CUDA/etc. as a back end without having to hand-write/-transform/-optimize it is indeed the point.

I'm extremely dubious that such an opaque abstraction can actually solve the (true) problem. "Not having to write CUDA" is not enough - how do you tune performance? Parallelization strategies, memory prefetching and arrangement in on-chip caches, when to fuse kernels vs. not... I don't doubt the compiler can do these things, but I do doubt that it can know at compile time what variants of kernel transformations will optimize performance on any given hardware. That's the real problem: achieving an abstraction that still gives one enough control to achieve peak performance.

Edit: you tell me if I'm wrong, but it seems that std::par can't even use shared memory, let alone let one control its usage? If so, then my point stands: C++ is not remotely relevant. Again, avoiding writing CUDA (etc.) doesn't solve the real problem that high-performance language abstractions aim to address.

Memory bandwidth is often the problem, yes. Language abstractions for performance aim to, e.g., automatically manage caches (that must be handled manually in performant GPU code, for instance) with optimized memory tiling and other strategies. Kernel fusion is another nontrivial example that improves effective bandwidth.

Adding on the diversity of hardware that one needs to target (both within and among vendors), i.e., portability not just of function but of performance, makes the need for better tooling abundantly obvious. C++ isn't even an entrant in this space.

I do appreciate the arguments that footnotes can be distracting, or that one doesn't know whether to skip them, but at present I see them as the best option for keeping the main body streamlined/as short as possible without sacrificing points that I'd like to make that wouldn't make for or fit into an appendix.

The authors are biologists, so I suspect they're not particularly versed in mathematical writing (and that McCarthy was not likely providing them much advice on it).

> Keep sentences short, simply constructed and direct. Concise, clear sentences work well for scientific explanations. Minimize clauses, compound sentences and transition words — such as ‘however’ or ‘thus’ — so that the reader can focus on the main message.

Repetitive sentence structure is not engaging and lulls a reader to sleep, no matter the context. Clauses and transition words and nontrivial sentence structure allow for qualification and clarification, juxtaposition and contrast, and emphasis, often with many fewer words than if written as a series of single independent clauses. A short sentence following longer ones punctuates its point and can effectively lead into subsequent sentences that express more complex ideas/explanations.

In my own scientific writing I also frequently use compound sentences to indicate that the ideas are related (causally or otherwise). It's also unclear to me how one could more efficiently communicate logical or causal flow between ideas than with transition words like "thus" or "therefore."

> Commas denote a pause in speaking.... Speak the sentence aloud to find pauses.

made its way into this article. Hard to imagine that this particular point, to which I might attribute many of the comma splices I see in scientific writing, actually came from a professional writer.