I've stopped using box plots (2021)

https://nightingaledvs.com/ive-stopped-using-box-plots-should-you/

by colinprince • 2 years ago

371 242 2 years ago

nightingaledvs.com

jcims• 2 years ago

What about violin plots.

https://en.m.wikipedia.org/wiki/Violin_plot

3170702• 2 years ago

I was thinking the same thing while reading, but the author does mention them at the end (together with the bee swarm plot or sina plot, which I think is the better version of a violin plot)

https://www.rhoworld.com/i-swarm-you-swarm-we-all-swarm-for-...

Scea91• 2 years ago

I use violin plots but a complication is that the shape depends upon the bandwidth hyperparameter of the kernel density estimator that is used inside. The plot can differ a lot for different bandwidth values.

Selection of the 'proper' bandwidth is a classic bias-variance tradeoff problem.

IshKebab2• 2 years ago

While true, that's not an additional problem compared to box plots which effectively just set the bandwidth to maximum. So IMO they are strictly better.

mjfisher• 2 years ago

The author mentions those at the bottom of the article, but two problems highlighted still remain:

* There's another intermediary concept (kernel density estimation) between the audience and the data

* They're still likely to misrepresent tight groupings and discontinuities, which will be smoothed out

adammarples• 2 years ago

Histograms and box plots are just clunky kernels density estimates too

bdjsiqoocwk• 2 years ago

The author just has a bad intuition. On the first picture he says "this looks like a small quantity". No, you can't say that. All you can say is that half the data points are in the shades part. You don't know where the rest are.

Jaxan• 2 years ago

I don’t think intuition is the right word. If you have never seen a box plot before, your intuition will not help parse it. (Unlike violin plots.)

wyldfire• 2 years ago

In my experience of sharing violin plots with people who are unfamiliar with them, it's not intuitive that the curve represents the distribution. Even with the scatter plot over/underlaid.

But that's okay, I don't mind explaining it and then the graph is easier to interpret imo.

wesleywt2• 2 years ago

You need to develop the intuition in the first place to read box-plots. The author argues that there are other plots where you don't require intuition.

ncruces3• 2 years ago

You don't know where the rest are.

Of course you do: they're in the whiskers; half in each whisker.

That's the entire point of the picture, BTW.

lkdfjlkdfjlg5• 2 years ago

You're right. I guess that's not the author's mistake then. His mistake is assuming "the whisker is small, therefore it has a small number of datapoints".

kzrdude• 2 years ago

The "this looks like a small quantity" comparison is wrong, because it's pointing to the lowest quartile, which has a cutoff which looks like 0 to <8 or so. While the histogram count compared to is using a bit of 0 to <10 - so it's not comparing the same counts, unfortunately. Having the historgram also count quartiles (or bins that add up evenly to quartiles) would drive that point home a lot better.

Apart from that quibble, it's a point very well taken.

rhdunn• 2 years ago

When profiling slow queries/code I often collect the elapsed time of a test where I take 5-10 runs and calculate the mean/average, standard deiviation, min, and max.

As well as using line charts on the average, I've used a box plot (with the edges of the box being the mean +/- 1 standard deviation) to get an idea of whether a given change is significant or not. I.e. if the boxes are close together I will ignore a change I've made, only committing changes that provide a significant jump in performance. The box plot is a useful way of visualizing that.

They can help with seeing highly variable performance (long box) from consistent performance (narrow box).

I can see this in the data (mean, standard deviation) but having it represented visually can help -- especially looking at the data over several iterations, or when looking for patterns from changing a variable (like the number of items in the data being processed).

I've also used linear regression calculations when data has looked linear or quadratic to check/confirm that assumption. -- You can overlay that on top of the data by computing the values for each value of n along side the actual data average and then including the average and calculated values in a line chart.

SillyUsername8• 2 years ago

So the diagram should not be used because of an education problem with some audiences?

Isn't that a bit like banning cars because some people can't drive?

Some diagrams are simply not for mass consumption and this is one, particularly because it is designed to illustrate an interpretation of ranges instead of the direct/linear representation of the raw data.

Of course I'd illustrate this fact as a Venn diagram comparing "box diagram" Vs "people" (intersection those who understand it) but I'm afraid the universal set may be mistaken as "those people who don't have eyes" rather than literally everything else.

Perhaps we should stop using that too, since it's non obvious what the universal set is.

All diagrams have some ambiguity and can be misinterpreted, sometimes it's deliberate (e.g. bar chart vertical axis not starting at 0 or scale not being linear) and that's why there's the saying "There's lies damn, lies, and statistics." That doesn't mean some diagrams are not useful, just that it's not suitable for some audiences who may misinterpret the data.

3170702• 2 years ago

It's not like banning cars, it is like banning horse carriages on high ways.

We have better technology nowadays, including for plotting, so why not ditch the old?

The author of the blog post has some good arguments. From your post, I cannot distill an argument as to why you would prefer specifically a box plot over a strip plot.

SillyUsername8• 2 years ago

Yes the other diagrams are better for mass consumption, and illustrating direct representation of the data distribution.

But that's not the purpose of a box diagram and the article even did a side by side comparison showing an apples and oranges comparison of 2 total different representations of the data.

Those diagrams were never meant to represent the data in the same way.

The article simply could have shown a better way of illustrating the data, rather than implying box diagrams are incorrect, which they aren't, any more than choosing a bad graph or axis is (CF. parent comment)

mkl3• 2 years ago

When there are alternatives that are clearer and also don't have this education problem, why use box plots? You seem quite keen on them, but why?

SillyUsername8• 2 years ago

There are a few advantages (see visualization section here pls http://en.m.wikipedia.org/wiki/Box_plot ) but my main concern is that the problem is not with the diagram, it's with idea that it's somehow faulty.

Sometimes you may want to highlight some core representation of data without the distraction of outliers (yes that does mean some people will use it for deliberate misrepresentation). But in this regard it's useful, as is on bar graphs not starting the vertical at 0 (because you want to illustrate relate difference not absolute amounts).

quenix• 2 years ago

Driving isn't a medium of communication, so this is an apples to oranges comparison.

If a medium of communication is misunderstood and found to be misleading to your audience, it doesn't really matter whether it's an education problem or not. It ceases to be a good communication medium.

The entire purpose of data viz as the author discussed is to convey ideas to other people. The author argues that people tend to misunderstand this specific chart type. It is valid, then, to dismiss the visualisation as bad for public communication.

Unfortunately, the technical merits of these things don't matter if most people don't understand them.

SillyUsername8• 2 years ago

As I've mentioned in other comments less succinctly, data hiding is sometimes useful of for drawing attention to other areas.

There are the better graphs the author mentioned for general purpose use, but the graph itself isn't at fault any more than using a bar chart with a poor scale (e.g omit 0-20) to do the same hiding.

JumpCrisscross2• 2 years ago

Driving isn't a medium of communication

There is a lot of implicit (e.g. traffic signals) and explicit (e.g. indicators and horns) inter-driver communication that is at the heart of most crashes.

cqqxo4zV46cp4• 2 years ago

If this is your approach, the only way you ever could’ve made anything actually useful is by sheer coincidence. Box plots, and their alternatives, are communication tools. Do you not care to find a more clear way to communicate? In drawing an immediate comparison with banning cars, you’re being completely unjustifiably standoffish.

sloowm5• 2 years ago

Why would you even use plots at all. You could just show the numbers for the 4 points represented in the box plot and people with proper education would understand. If people need diagrams it's just an education problem with some audiences.

But the real education deficit shown here is psychology education. Humans are bad at doing some calculations inherently. They are not able to properly asses pie charts and easily confused by numbers with a lot of digits. Even before these studies were done people were able to come up with visualizations that were better suited for human understanding.

People chose to use box plots because the visualization was better to understand by people than the numerical representation of the same information. Luckily there are now even better tools to represent the same numerical data in a way that is even better to understand.

So, if you are truly educated properly you don't use visualization.

munch1176• 2 years ago

So the diagram should not be used because of an education problem with some audiences?

A problem like this one that he mentions, "People associate longer shapes with greater quantity", is not something you can fix by teaching. Even if you know intellectually that the association is, in this case, wrong, you can't free yourself from the association. It's hardwired into the brain.

People who work with this sort of diagram a lot will eventually build up context-specific associations that work better, overriding that instinct, to the point where it feels seamless. But even if it feels seamless and easy, the dissonance is still there, and may lower your comprehension speed and slightly impair your judgment.

As a statistics expert, you are never going to notice that, because your baseline comprehension speed and judgment on the subject is so good, that this very minor impairment is lost in the noise. So you may not be a good judge of the usability qualities of the diagram type.

ekianjo• 2 years ago

just use boxplots with an overlay of the actual data and any confusion goes away

flumpcakes• 2 years ago

This is the way to go in my opinion. I think it’s the easiest, most straight forward, and not confusing to the reviewer. You shouldn’t be using box plots to describe the shape of data to begin with, but having a ghost/after image/super imposition can probably only help in cases where you need to communicate that the shape is different, even if the statistical nature is the same.

cjk211• 2 years ago

No you shouldn’t stop using box plots. You should use them for when they are appropriate - showing location and spread. And not shape! There’s absolutely no information on modality or distribution presented past quartiles and limits.

They are mostly useful for comparing batches not analysing an individual batch.

The author doesn’t know what they are talking about and is telling people as if they do. If he read any of Tukey’s material he might know. But no name dropping is enough clearly…

ohmyiv3• 2 years ago

No you shouldn’t stop using box plots. You should use them for when they are appropriate

Yes, the author is aware of that. They even stated so:

Despite making more visual sense than box plots, I still wouldn’t recommend these design concepts or box plots in most situations because…

Seems a few people missed the "in most situations" part. He's saying he stopped using them for whatever reasons because it isn't working for his audience. So as the title suggests, maybe we should all take a look at our use of box plots and see if there are better alternatives.

Also remember who he's talking about when it comes to reading box plots. He's not talking about people who understand box plots. He's talking about others that don't know or understand box plots, which seems to be thousands of people he's had to explain it to, according to him.

cjk211• 2 years ago

The author doesn't use the correct terminology and does not understand box plots themselves so they are in no position to explain them to anyone. They explain in terms of absolutes with no rational or scientific explanation and entirely miss the point of the methodology and tools. That is a not a good position to start or a good person to take advice from.

Not only that, the cases presented are likely better dealt with via inference tests. But the author's knowledge doesn't extend that far. And even going as far left, the posed question isn't even defined in the article. So how was a suitable methodology chosen? Well it wasn't - lets just throw this pretty picture up and whine about it.

The author is way out of their depth and should retract the article and take a formal, accredited statistics course.

nickdesb4• 2 years ago

To clarify, I (article author) am not aware of any scenarios in which a box plot would be a better choice than simpler chart types, even for very sophisticated audiences. From the article:

"Other reviewers suggested that the conclusion [of this article] should be that box plots are a useful chart type, but only for statistically savvy audiences. Again, I’m going a step further, suggesting that even those audiences would be better served by other chart types in virtually all situations."

sloowm5• 2 years ago

You absolutely should stop using box plots. The only reason to use them is because you have to draw a representation by hand and do not have access to a computer.

A box plot is a data compression technique for compression by hand. There are now better automated techniques that both preserve data quality and visual quality better.

magnio• 2 years ago

You are looking at this as a technical problem, where box plot is a compact visual representation of variance and outliers that is perfectly perfunctory as it is cromulent.

The author is approaching this as a human problem. Plots are not made for machines, they are for people to read, and the author specifically wants as many people can read and parse plots easily as possible. As lamentable as math education might be, we have to work with what we have, and I do think it is a reasonable goal. I agree with the author that it should not be necessary to know what quartiles are in order to see how spread out a distribution is.

cjk211• 2 years ago

So your approach and the author’s is to dumb a technical measure down to a level where the observer doesn’t need to understand what they are looking at.

Well that explains the entire data visualisation and dashboard consultancy nicely.

How does anyone rationalise the information they have if they don’t make an effort to understand it. Or how can they even select a visualisation method or comparison method. We are truly fucked!

psyklic• 2 years ago

Box plots make distributions easier to reason about by oversimplifying them. In a similar way, the mean can be very misleading (but we likely won't forbid its use!).

IMO a good takeaway might be to always use a plot that fairly represents the underlying distribution.

sigmoid104• 2 years ago

box plots always make distributions look bell shaped

I feel like this is where the confusion stems from for the author and everyone else here. Box plots don't make anything bell shaped (they don't change the distribution), they assume that your data follows a bell/gaussian shape. This is correct in cases where the central limit theorem can be applied (which is almost everywhere) - but when that is not the case, the assumption is wrong and you shouldn't use a box plot anyways, because the values it shows have no real use. There are very real use cases for box plots, but people need to understand the basics of statistics before they can use them.

blueflow12• 2 years ago

This should be the topmost comment. Box plots are made for visualizing generalized normal distributions and nothing else.

Edited to preempt nitpick.

cjk211• 2 years ago

This is also wrong. Gaussian curves are symmetric. Box plots do not have to be. In fact representing skew in a batch is one of the fundamental purposes of them.

pocketsand3• 2 years ago

Why? They’re non-parametric and make zero assumptions of normality.

munch1176• 2 years ago

But is that what they're actually used for?

The data has been reduced to three numbers, throwing away most of the information that you would need to assess whether the distribution is gaussian or not. If it's not, how will you ever know?

cjk211• 2 years ago

Exactly this on the last point. Although rereading this the distribution point is explained poorly.

People waltz in with assumptions and then complain when they don’t work because they don’t really understand the tools they are using. The author is one of them. It’s a bad article and the author should not be using or demonstrating things they clearly don't understand.

munch1176• 2 years ago

Isn't that the whole point? That the graph type is very easy to misunderstand. If you are right, and not even a professional data visualization consultant properly understands the graph, then who will?

Beldin• 2 years ago

Box plots [...] assume that your data follows a bell/gaussian shape.

Not sure how to square that with this statement on Wikipedia's page on box plots:

Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution[3]

sigmoid104• 2 years ago

If you want to see why that is not fully correct you should read the article. For a box plot you need to calculate mean, variance and certain percentiles. These values don't make sense if your distribution does not follow a certain shape (because these values unambiguously define such a shape). See the examples in the article for what happens if you still try to use them in those cases. You can still extract the values of course (hence probably why wiki says they don't assume anything), but you lose significant information about the distribution. So you can no longer reverse the process.

treflop2• 2 years ago

I agree. The author simply used the wrong chart.

The author's example has a bimodal distribution (TWO peaks) and chooses a type of chart that has ONE peak (a box plot).

A little baffling tbh.

rcxdude3• 2 years ago

Well, to start with, how would you determine that about your distribution in the first place? And if that works well enough, why use a box plot afterwards?

thaumasiotes6• 2 years ago

and chooses a type of chart that has ONE peak (a box plot)

Huh? A box plot doesn't have any peaks. A box plot is a histogram subject to the constraint that every bar in the histogram is equally tall. There can never be more than zero peaks.

ozyschmozy• 2 years ago

There are very real use cases for box plots,

The author argues otherwise, can you give an example of a use case where box plots would be preferable to the alternatives the author suggests?

ohmyiv3• 2 years ago

There are very real use cases for box plots,
The author argues otherwise

No, in the article he says he wouldn't recommend them _in most_ situations. It's a part that a lot of people here seemed to have missed whether arguing for or against box plots.

Despite making more visual sense than box plots, I still wouldn’t recommend these design concepts or box plots in most situations because…

(Emphasis mine)

cjk211• 2 years ago

Comparing location, spread and skew of multiple batches.

rzmmm• 2 years ago

Often people are interested in exact quantitative statistics like IQR, median, top/bottom deciles which are commonly represented in box plots. The alternatives are visually simpler but they contain less quantitative information.

rcxdude3• 2 years ago

Nah, there's nothing in a box plot which assumes a bell-shape. It does, however just visualise the parameters which reasonably well characterize a smooth single-mode distribution regardless of the underlying distribution. So it's a valid criticism of using box plots, especially when the alternatives can just as well visualise a bell-shaped distribution, as well as showing when it is not.

quietbritishjim• 2 years ago

smooth single-mode distribution

That IS a bell curve. While it's true that the Guassian distribution is often called a bell curve or even "the" bell curve, a non-Guassian single mode distribution is still absolutely bell shaped in a general sense.

So, although you started your comment with "nah", you're actually in agreement with the content you replied to.

kazinator• 2 years ago

The problem is that the four quantile groups contain equal numbers of items, but are not represented by equal areas, even if we replace the whiskers with a bar of the same width as the box.

The bottom whisker contains 25% of the data, yet is just a thin line, which can furthermore be arbitrarily short.

It really is a dumb visual presentation.

The only way to use it is to recover the five parameters from it, and then stop looking at it.

For that purpose, a QR code would be just as good, if not better. You'd need a device with a camera to get the parameters (but "everyone" has that now), and when you're looking at it with your bare eyes, it doesn't tell you any visual lie.

wesleywt2• 2 years ago

This is exactly why the author says you should stop using Box plots. The plot is easy to misinterpret.

IanCal2• 2 years ago

they assume that your data follows a bell/gaussian shape

No they don't. They show quartiles mostly, and don't assume symmetry or any parameters of a gaussian.

kamma4434• 2 years ago

What you say is technically correct, but in the sense where you can put rat poison in One of those ceramic cookie jars they sell in houseware shops. There is nothing wrong in doing it, but it may lead to interesting failure modes Because someone can have implicit assumptions about what’s in there.

amelius2• 2 years ago

A bell shape has no minimum/maximum, like the box has.

Hendrikto• 2 years ago

In theory. In pratice you always have a finite sample size and thus a min and max.

lkdfjlkdfjlg5• 2 years ago

Boxplots don't assume anything about your data. They just measure percentiles and put them on the y-axis.

vehemenz• 2 years ago

The argument is more about the relation between the visualization and the audience, not the data and the visualization. I see a lot of commenters missing this point.

jncfhnb3• 2 years ago

people need to understand the basics of statistics before they can use them.
they assume that your data follows a bell/gaussian shape. This is correct in cases where the central limit theorem can be applied (which is almost everywhere)

You sir just failed basic statistics

fnordpiglet2• 2 years ago

Sorry I don’t understand. The central limit theorem describe the distribution of the sample means from a population. It describes the distribution of the mean, not the distribution of the population itself. The shape of the distribution of the sample mean isn’t super interesting when you’re interested in the distribution of the samples themselves as a proxy for a population. So I’m not sure I understand your assertion. Could you explain more your reasoning? Maybe I’m missing something, but the estimation of the sample mean distribution isn’t the only metric that’s useful, and almost nothing in nature is normally distributed otherwise. Normal distributions are generally a useful assumption mostly because of the analytic form of the Gaussian and our understanding of how to work with it. But that estimation isn’t useful as it might seem. A Poisson distribution is much more common for instance.

sigmoid104• 2 years ago

I't appears you don't understand the central limit theorem fully. You gave the definition you find in textbooks, but you don't see how it applies to real world measurements and already explains your question. I can only recommend to visit a university level statistics course at this point. Maybe you will understand when you actually deal with some real data. Then you will indeed see its consequences pop up everywhere. The issue (also for the blog author) is that it is often implicitly assumed. It is one of many common pitfalls in statistics. You should also learn what the difference is between a poisson and a gaussian distribution. They may look similar, but there is a drastic difference in their definition and they are used in very different circumstances.

crazygringo7• 2 years ago

Yes.

A lot of people here are commenting that no, technically box plots don't assume any distribution. And I mean, technically you can ride from NYC to SF in a lawnmower.

But I completely agree that box plots shouldn't ever be used for anything but unimodal distributions similar enough to a bell/gaussian distribution.

All of the criticism of the article seems to be that they're misleading when the distribution is not bell/gaussian, e.g. bimodal.

To which my reply is, of course. Box plots shouldn't be used then. But if your distribution is bell/gaussian, they seem fine and I see no particular issue with them.

pictureofabear• 2 years ago

This article is very click-baity.

Boxplots are a single tool for data analysis. They do not apply in every situation, nor do any other tools. The same goes for pie charts, which are constantly being accused of always distorting data. Pie charts, like box plots, have their place.

hoosieree• 2 years ago

Murphy's law for data viz:

If a plot can mislead, it will.

theamk2• 2 years ago

Well, how do you readers know if your distribution is bell/gaussian? Sure, sometimes you plot means of large samples, and then it is true by construction; but a lot of time people use box plots when there is no intrinsic reasons for data to be gaussian. Like most experimental papers.

Or take the first example from wikipedia page on box plot [0]: "Box plot of data from the Michelson experiment", which is just 20 points per run. Would I want to see this in the paper? No please. There is no evidence that the experimental data is gaussian (or even single-modal). Or further down that page, "A series of hourly temperatures" - why would one box-plot it either?

And even if you claim your data is gaussian by construction, maybe because you surveyed lots of people - I still want to see the evidence, as it's pretty simple to make experimental mistakes that turns data non-gaussian (say you only surveyed two neighborhoods with very different properties)

In other words, the domain where box plots are sufficient is very small. Most publications should never use them.

[0] https://en.wikipedia.org/wiki/Box_plot

mannykannot• 2 years ago

The article's full argument seems to be that there are alternatives which are applicable where box plots are not and, at least in most cases, better where they are (there is a tacit (IIRC) subtext of "given that we're using software to do the plotting.")

This is debatable, but noting that box plots are satisfactory for unimodal gaussian-ish distributions is not a very persuasive response.

imachine1980_• 2 years ago

Could you please elaborate on the reason? I assume it’s related to a unique null derivative instead of multiple maxima, but I couldn’t find any papers or information on this.

Additionally, I find the article informative but believe it could be improved with this clarification. As someone who has worked with data analytics but is not a mathematician or actuary, I know people who probably review these types of graphs. Now, I understand that it is essential to check the underlying data distribution to avoid being misled by the information, even if the source and axes seem trustworthy

sigmoid104• 2 years ago

related to a unique null derivative instead of multiple maxima

I think the word you're trying to use is "bimodal" and yes, that is one example where the author's reasoning fails. But it's not the only one.

I couldn’t find any papers or information on this.

You said you have no formal higher education in mathematics - how would you even go about finding (let alone understanding) papers? Regardless, just to be clear, this is not something you would learn from papers but from introductory textbooks and university courses. Everyone who has to deal with statistics in science needs to go through a whole lot of extra education exactly because there are many pitfalls like this.

it is essential to check the underlying data distribution to avoid being misled by the information

That is another half-truth that everyone on the outside seems to agree on, but it is useless in practice. What do you do if the underlying data is not accessible. And what if you don't have the means to process it for every paper you read (which is what usually happens)? Then you have to rely on the actual tricks of the trade, which will come naturally if you worked with tons of statistics before. There are lots of telltale signs that let you spot bad analyses by only looking at a plot or summary chart. Granted, you won't catch all of them, but it often takes real malice and deep statistical competence on the author's side to cover up these things.

kylebenzle• 2 years ago

Yes! You are right and my gears were grinding the whole time reading that article because right of the bat they make some gross and incorrect assumptions.

A box plot isn't trying to show the same thing a histogram is, it's like saying we should stop using Venn diagrams because they confuse people when trying to show the exact amount of overlap, so pie charts are better...

It's silly.

chipdart• 2 years ago

Box plots don't make anything bell shaped (...), they assume that your data follows a bell/gaussian shape.

Not true. Box plots represent low and and high quantiles independently and support the representation of outliers.

This alone is enough to make it clear for everyone that they don't require a distribution to be symmetric, let alone bell shape.

mkl3• 2 years ago

The only advantage box plots had is that they can be drawn by hand. Now that computers are ubiquitous this is no longer valuable.

Violin plots and bee swarm plots are better. Jittered strip plots can be okay if you're careful to avoid saturation (or more points added in the saturated region will disappear as they can't make it any darker).

frodo8sam• 2 years ago

I'll take a plain histogram/kde plot every day of the week over those damn violin plots. I think box plots are quite usefull as they are easy to read but only if you trust the author has actually looked at the histogram. And you can typically not trust the author to have done that.

mkl3• 2 years ago

Violin plots essentially are KDE plots, but you can put multiple of them on the same axes to compare groups.

medstrom• 2 years ago

Perhaps you'd find the half-violin plot more readable? Seems there's a whole world of all-in-one "raincloud plots" that integrates them, like the lower infographic here: https://raw.githubusercontent.com/Z3tt/TidyTuesday/main/plot...

You can even make 'em show histograms: https://miro.medium.com/v2/1*J3Q4JKXa9WwJHtNaXRu-kQ.jpeg

klysm4• 2 years ago

A violin plot is literally just a KDE sideways.

jhbadger• 2 years ago

I'm surprised the article just briefly mentions violin plots. Those are becoming popular in biomedical research -- much more common than the plots he suggests. And you can always overlay them with the jittered points if you want too.

j_bum• 2 years ago

I disagree about violin plots being better.

Here is a great rant (borderline lecture) from Angela Collier on why they aren’t [0]

[0] https://youtu.be/_0QMKFzW9fw?si=86mRAZRnFCBfSzw0

sanderjd10• 2 years ago

Could you summarize the criticisms in this (pretty long) video, and what she is proposing as a better alternative (beanplots? or is she criticizing those too?)? I couldn't figure it out from perusing the transcript.

I think it's useful to be able to compare the approximate shapes of histograms during exploratory data analysis. Is the thesis of this criticism that this isn't actually a useful thing to do, or that violin plots don't achieve this, or is it "just" an aesthetic argument?

klysm4• 2 years ago

100% on the money. Box plots are an archaic technique for working around limitations that no longer exist.

y42• 2 years ago

In short and unsurprisingly: Not every analysis and data set works with every visualisation.

montebicyclelo• 2 years ago

The author has experience of teaching box plots in various organisations.

The author has found that compared to other types of plots, people struggle to learn how to intepret box plots.

The author proposes some alternatives that they believe to be easier for people to interpret:

- Strip plots (for few data points)

- Jittered strip plots (for more data points)

- Distribution heatmap (for even more data points)

----

This aligns with my experience of trying to convey information to non-technical or moderately technical people; box plots are a struggle for them. To me it does seem like the proposed alternatives would be more accessible.

Sure, we could try to better educate people about box plots, (as the author has done professionally); or we could consider using something that requires less effort for people to comprehend.

scrollaway3• 2 years ago

Yeah I'm shocked at the awful quality of comments here. This is a clear and straightforward article laying out the issues with box plots and appropriate alternatives, from a professional who works in the field and spends his life explaining these.

And still half the comments are like "But I know better!"... yeah, I'd wager most here don't.

SillyUsername8• 2 years ago

I'm qualified in maths related computing and statistics to exam invigilator level, if that helps offset your bias.

SillyUsername8• 2 years ago

I'm not suggesting that the other diagrams shouldn't be used, just that box diagrams aren't wrong, they hide data, which is sometimes useful.

I wish we could educate everyone in the ways data can be misrepresented - scale, non 0 axis starting, omitting categories, combining groups, colours, point sizes not representative of data - and they can all be levelled at other graph types, singling out box plots for hiding is no different, but IMHO not justification for not using them with the right audience.

wodenokoto• 2 years ago

I’m a big fan of the jittered strip plot and I often ad special logic to color dots at the edges of a largish gap. This is super useful if you are plotting the distribution of daily messages and just plotting dots will hide that there are days without messages

These3352• 2 years ago

Sure there are alternatives and I agree with the author's criticisms overall. But boxplots are a staple in statistics, and if your audience can reasonably be assumed to have some level of statistical training then boxplots are perfectly reasonable in my opinion.

cqqxo4zV46cp4• 2 years ago

Would you care to address the specific argument that the author makes about not using box plots with audiences? I swear, statisticians are among the most inertia-prone groups of people that I’ve ever worked with. You need a certain degree of “do it this way because it’s done this way” to deal with the amount of BS going on in this field.

sloowm5• 2 years ago

Are you sure that well trained audiences are able to accurately asses box plots. For instance, most drivers think they are better than average drivers.

It being a staple in statistics is also not a good argument. The information conveyed through box plots is used in lots of fields with different education backgrounds. If a visualization, which in itself is a human simplification of data, is hard to understand, it will be misunderstood by some. This means these people will not be able to advance their field of research as well as with better visualization methodologies.

CuriouslyC• 2 years ago

Box plots are a relic of a time when we couldn't print really nice charts. You can just display the distribution in line like a scrolling oscilloscope/topographic display, or you can do a density plot over time (look at gaussian processes) and overlay shaded regions for important time periods.

zaptheimpaler• 2 years ago

I always find new types of plots very interesting. Is there a nice resource showing all the common types of plots, when to use them, alternatives, code etc?

amelius2• 2 years ago

https://matplotlib.org/stable/plot_types/index

https://d3-graph-gallery.com/

cb3212• 2 years ago

The @amelius sibling has nice links to "graphics" choices, but I feel like the overall topic of the original article and this comment thread is more about the interaction of that with "statistical choices" as per my other comment (https://news.ycombinator.com/item?id=40766618) pointing to plots you might like to peruse.

For example, though the final example in the reference there is graphically "only" shading the "outer band" darker than the inner alpha-blended region, this seems important statistically/visualization-wise since the unknown true parent distribution/ensemble samples are, well, sampled from need only be any monotonic curve within the whole region.. (not even differentiable if mixed discrete-continuous values may happen).

singingfish• 2 years ago

And no mention of notched box plots which make a lot of the troublesome aspects go away?

kkfx• 2 years ago

Honestly? I do not care much about charts in general, while I do care much about the availability of the data used to produce a chart... In way too much cases I see plots and no data, sometimes data are there but not easy to use, and another thing I do care is the ability to tweak a graph.

The above are between the reasons I prefer remote meeting where data are to be shown instead of in person: anyone attending should have a computer ready to use and IF data are shared and ready usable I can live tweaks a plot ad reason on it while I listen end eventually pose relevant questions shown at my own turn something. Surely not all presentations are meant to be interactive session, but being able to interact even in async form reading a journal article, playing with the data and eventually drop a mail to the author is a nice thing, typically uselessly hard today where in tech term it can be extremely simple.

That's another reason I have presentation software/office automation one instead of plain org-mode, Jupyter, R Studio etc because change things it's hard while it should be easy. Org-mode is excellent to present but not really interactive, I have to regenerate plots to see changes or push data to external software, Jupyter is not really meant to present, R Studio offer nice LaTeX integration and tabular view but do not offer nice means to present, though they are still FAR better then presentation software and even if have some safety aspects to be taken into account I prefer countless of time receiving an active document (org-mode, jupyter notebook etc) instead of a pdf or even worse some office formats.

karmakaze2• 2 years ago

There are other distribution chart types that can be useful in specific situations, such as frequency polygons, violin plots, cumulative distribution plots, and bee swarm plots, but the three types that I described above are the easiest ones to grasp, and are able to communicate most of the insights that are needed for day-to-day decision-making in most organizations. (I’m not mentioning histograms here because they’re generally only useful for visualizing a single set of values, whereas box plots and their alternatives are for visualizing multiple sets of values, which is a different use case.)

There's generalizations and 'specific situations' which the author considers worthy of some plots, and other specific situations that the author doesn't consider worthy of other plots. At best, don't use box plots if your distributions do not have a single mode and may likely be misinterpreted is my takeaway. Here's a rant against violin plots by my fave physicist ranter[0] (not Sabine), so maybe never use them.

[0] https://youtu.be/_0QMKFzW9fw?si=4VM4DT9Q1zEnV93A

cb3212• 2 years ago

People have conflicting goals. On the one hand they long to compress many numbers into one or a few summary statistics. On the other hand, the moment such lusted after summaries mislead in some way they regret the data compression. What's really going on is that people want a simplicity (often in the form of definite conclusions) which may just not exist. This is really a common malaise of the human condition.

Similarly, the distribution represented by a box plot itself is often the distribution of "just one sample". When viewed as such, a distro has its own uncertainty[1] and that uncertainty is not represented in a violin plot, for example. As with every "right tool for the job" debate, people will vary based on experience with the tools, including how to simplify/explain them to others.

[1] https://github.com/c-blake/bu/blob/main/doc/edplot.md

riedel• 2 years ago

Actually you may nicely integrate box, violin, bee/scatter plots [0]. For simple visual ANOVA testing box plots are great. On the other hand violin plots are great to quickly check distribution assumptions for testing and together with scatter plots give you a good impression of the sample.

[0] https://davidbaranger.com/2018/03/05/showing-your-data-scatt...

iainmerrick2• 2 years ago

Lots of people defending box plots here -- a lot more than I expected!

What I don't see is anyone saying "box plots are useful because they're the best kind of chart for [specific use case]". I can't off-hand think of any situation where I'd rather see a box plot than a strip plot or violin plot. When and why would you want to summarise the data so coarsely and visualize it so un-intuitively?

lkdfjlkdfjlg5• 2 years ago

What I don't see is anyone saying "box plots are useful because they're the best kind of chart for [specific use case]".

Box plots are useful because they're the best kind of chart for when I have multiple populations and I want to quickly glance whether it's reasonable to assume that the populations have the same median, or not (you do that comparing not just the medians of the populations but also the shaded areas)

johnbcoughlin• 2 years ago

I can't see why a jitter plot with dark lines marking the quartile wouldn't be strictly better for this.

weebull2• 2 years ago

If you're only comparing medians, then just plot the medians. Why a box plot with the quartiles?

DonsDiscountGas• 2 years ago

Violin plots are massively overhyped, IMHO. If your data is simple and unimodal, use a boxplot. If the distribution is more complicated and you need some detail, use a histogram or a ridge plot. Violin plots are never the best option; they're curvy so a little more pretty but don't do a good job of conveying information.

inciampati• 2 years ago

They really help when you're working with huge numbers. It's just a different kind of density plot. A vertical histogram can be nice too. Or you can use color and overlay a few regular old histograms. Go wild.

weebull2• 2 years ago

If your data is simple and unimodal, use a boxplot.

How is the reader to know you've used the right plot? How are they to know that you haven't hidden a bimodel dataset behind a box plot because it makes your conclusions easier?

If the distribution is more complicated and you need some detail, use a histogram or a ridge plot. Violin plots are never the best option; they're curvy so a little more pretty but don't do a good job of conveying information.

They are just multiple, non-overlapping histograms plotted next to each other. They allow you to compare distributions without them getting in the way of each other.

I can understand if it's the fitted PDF that you think hides the original data. That is unnecessary IMHO.

kaitai• 2 years ago

I deal with a lot of business people who have processes that rely on 15th/85th percentile, or 25th/75th percentile. They want to see the median, the low/high percentiles, the max/min or outliers, and they don't want to see all the data points jittered in between. It's just overwhelming extraneous information. They in fact like tables with those numbers written down, but they want to compare ten different (time series of historical prices for different markets) and see it on one Powerpoint slide. The box plot allows a fast visual comparison of medians and other key percentiles (label the plot with the percentiles if you're doing something non-standard!). With jitter or violin they get hung up on weird random stuff and it derails meetings.

Important caveats: the generating processes for all these quantities are the same in a physical sense, so they are comparable. All the distributions are roughly lognormal-ish, so they are single-peaked distributions, as folks are discussing here. The point of the visualization in theses cases is not to understand the properties of the distribution per se, it's to show the important percentiles because they have business implications.

iainmerrick2• 2 years ago

That’s a good explanation, thank you!

callalex• 2 years ago

Who drew those boundaries at 15/85? What makes those boundaries useful or correct?

s1artibartfast2• 2 years ago

When and why would you want to summarise the data so coarsely and visualize it so un-intuitively?

Sometimes less is more; Box plots are specifically good for showing and comparing quartiles.

If you want to compare several groups and care about gross differences, they are an excellent tool. They are an excellent to when you believe the data is normal and think the histogram is misleading. they are also great if you think the data isnt normal but care about quartiles.

Any time you would be happy with a table of the 5 datapoints (min, max, median, 25th, and 75th percentiles), box plots a great tool for graphic comparison.

michaelhoffman• 2 years ago

Wherever possible, I use sina plots, which provide many of the advantages of violin plots while actually showing the individual data points.

https://en.wikipedia.org/wiki/Sina_plot

https://cran.r-project.org/web/packages/sinaplot/vignettes/S...

Adding on a representation of mean in a different style (like a black bar) can be helpful. So can a boxplot-style indication of variance, in some cases.

benrapscallion• 2 years ago

Do it the way Nature journals now require it to be done: show the underlying data points overlaid on the box plot. Best of both worlds.

inSenCite• 2 years ago

been in love with violin plots

chefandy• 2 years ago

Just like anything else in design, the first question should be "how can I convey this most clearly to the audience I'm addressing" not "hmm, I wonder if there's are any problems the technique I chose because it's what everyone seems to use for this." Use the right tool for the job. There's even a good chance that juxtaposing these elements differently or adding another element could clear this up entirely.

This is why it's good to have a really competent visual designer around. Their sole purpose is visual communication, and that very much includes dealing with the subconscious connotations and unintended messages hidden within data visualizations. Yes, you've probably encountered designers that would not be good at that, you imagine. You've also probably encountered developers that would not be good at the sort of data munging that scientists, et al do; that doesn't mean developers, generally, aren't best equipped to handle the related coding problems.

svara• 2 years ago

The alternatives he proposes have their problems too.

Just plotting points will lead to saturation in high density areas that depends on point size and opacity.

Making bin color proportional to point density will require normalization to make the plot readable in many cases.

While I like these plots too in certain situations, I would argue they're actually less elegant than the boxplots for those reasons.

And come on, boxplots aren't that hard to explain to someone who already is used to working with percentiles.

klysm4• 2 years ago

I think there is an aversion to just showing the damn distribution as a histogram or KDE. I hear arguments from product owners that it’s “too complex” etc.

jncfhnb3• 2 years ago

The author showed jittered strip plots where you plot each point correctly on the y axis and randomly offset the x axis.

These are ok but it’s hard to differentiate the density of points when they’re randomly offset. Try a swarm plot (seaborn) / bee swarm plot (R).

It’s the same concept but the points are strategically placed across the x axis to show the width of the distribution at each point. It generally looks much cleaner.

emilk• 2 years ago

Importantly, box plots are also ugly. Beauty matters.

Falkon13132• 2 years ago

I was not entirely convinced by the article, being used to box plots myself for several decades. I've used them in school, college, and at work.

But after having read these comments, it really drives home his point that you can get a room full of lots of very smart people who all know what they're talking about, and they'll all disagree about the understanding and interpretation of box plots.

It's a little surprising, but the evidence in these threads pretty much cinches the argument for me.

flusteredBias• 2 years ago

ECDF plots are what I use.

__mharrison__• 2 years ago

I've resorted to just teaching four plot types when I teach visualization.

- Bar

- Scatter

- Line

- Histogram

You can tell 90% of your stories with these plots. (If you pay attention to professional viz groups, Economist, NY Times, etc, they use these.)

Don't waste your time with other plots unless you have mastered these. When you master these, you will realize you don't need other charts.

moi2388• 2 years ago

I’m probably wrong, but this entire article felt as an advertisement for violin plots without it being mentioned once

pvaldes• 2 years ago

That problem has been solved long time ago. When a box plot is not enough, just use violin plots

On gnu-R:

install.packages('ggplot2')

?ggplot2::geom_violin

Kalanos• 2 years ago

Plotly has an option on box plots that shows the individual points as well, which I like better than violins

greentxt• 2 years ago

Just use a heat map instead. /s

nickdesb4• 2 years ago

As the author of the original Nightingale article that kicked off this (wild) thread, maybe I can clarify a few things:

My fundamental concern with box plots is that no one has ever shown me a single scenario in which a given insight was clearer in a box plot than it would be in a simpler chart type (i.e., strip plot, distribution heatmap, or stacked histograms). If someone can show me even a hand-crafted, cherry-picked scenario with the same data shown as a (well-designed) box plot AND a strip plot, distribution heatmap and stacked histograms, and in which a potentially useful insight is clearer in the box plot than in the other chart types, I’ll happily change my opinion. I’m still waiting for someone to show me such a scenario, though.

In the meantime, I’m not sure why one would use box plots when simpler chart types are available that say the same thing about the data or, in many cases, say more about the data (show gaps, multi-modal distributions, etc.). Even if the audience is very used to reading box plots, they’ll still find strip plots, distribution heatmaps and stacked histograms to be simpler to read (and will actually see gaps, clusters, etc.)

How do I know that other distribution chart types are simpler to read than box plots? Because I’ve taught these chart types to literally thousands of people of all skill levels all over the world. Quartiles are just inherently less intuitive than bins or, in the case of strip plots, no delimiters to understand at all.

Like I said, if someone can show me a scenario like the one that I described above, though, I’ll happily change my mind…

Before people jump all over me, I should clarify what I mean by a “potentially useful insight.” For example, “showing the interquartile range” is not an “insight” in this context, it’s an “observation” because it doesn’t point to any kind of action or conclusion, in and of itself. A potentially useful insight would be something like, “The employee salaries in Company A are generally higher than those in Company B.” or “Most people make close to $80K in Company A, but the salaries are much more spread out in Company B.” Basically, an “insight” in this context is a piece of information that would point directly to some kind of action or conclusion.

I've stopped using box plots (2021)

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