[HN Gopher] I've stopped using box plots (2021)
___________________________________________________________________
I've stopped using box plots (2021)
Author : colinprince
Score : 359 points
Date : 2024-06-23 06:26 UTC (1 days ago)
(HTM) web link (nightingaledvs.com)
(TXT) w3m dump (nightingaledvs.com)
| jcims wrote:
| What about violin plots.
|
| https://en.m.wikipedia.org/wiki/Violin_plot
| 317070 wrote:
| 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 wrote:
| 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.
| IshKebab wrote:
| 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.
| IanCal wrote:
| I find violin plots suggest far smoother results than
| actually exist so you need to be careful with the amount of
| data.
| IshKebab wrote:
| I agree but so do box plots. I think probably the best
| thing is violin plots when there's lots of data and bee
| swarm plots when there isn't. But either are better than
| box plots.
| karmakaze wrote:
| What about using rotated, symmetric histograms--like a
| quantized violin plot?
| mjfisher wrote:
| 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 wrote:
| Histograms and box plots are just clunky kernels density
| estimates too
| bdjsiqoocwk wrote:
| 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 wrote:
| 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 wrote:
| 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.
| wesleywt wrote:
| 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.
| ncruces wrote:
| > 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.
| lkdfjlkdfjlg wrote:
| 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".
| ncruces wrote:
| That's not his mistake. He knows this, but repeatedly
| failed to convey this to others.
|
| That's like the entire point of the post: they're hard to
| teach to others (they're unintuitive) and there are better
| (more intuitive) alternatives.
|
| I dunno if I agree, but it's ironic that this thread
| started with a poster complaining about the author's bad
| intuition, while apparently managing to not have a good
| grasp of box plots themselves.
| lkdfjlkdfjlg wrote:
| What are you talking about? I have a perfect grasp of
| these things. As I said, half is in the shape area. You
| must've missed that.
|
| Also, that IS his mistake, it's literally the first thing
| in the post. And this stuff isn't hard or hard to teach
| _at all_ has long as you're at least 5.
| ncruces wrote:
| This thread started with _bdjsiqoocwk_ , who wrote:
|
| > You don't know where the rest are.
|
| This is wrong, period. And the fact it's wrong is pretty
| much the entire point of the article.
|
| Are _bdjsiqoocwk_ and _lkdfjlkdfjlg_ the same poster?
|
| Please don't pick a needless fight.
| kzrdude wrote:
| 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 wrote:
| 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.
| SillyUsername wrote:
| 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.
| 317070 wrote:
| 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.
| SillyUsername wrote:
| 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)
| cqqxo4zV46cp wrote:
| IIn all of your replies you make snide reference to
| "general audiences", "mass consumption", etc. You very
| obviously place yourself in a higher class because of your
| ability to correctly interpret box plots. Can we please
| just move past that though? The vast vast vast majority of
| box plots are for "general consumption". The vast vast
| majority of box plots are used in place of a more suitable
| chart type. You seem to be arguing that, because a box plot
| is hypothetically suitable for some (in the grand scheme of
| things) corner case, that the author's point is faulty. I
| think that you are completely overstating the importance of
| the hypothetical 'correct case'. You're getting stuck on a
| point that nobody, least of all the author, is making.
| mkl wrote:
| 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?
| SillyUsername wrote:
| 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).
| Angostura wrote:
| The article doesn't really argue that they are "faulty"
| just that there are better alternatives in the large
| majority of cases. I think he makes a compelling argument
| SillyUsername wrote:
| Fair enough, it was the comment that "better-designed
| chart types" that caught my eye. "better designed for
| general use" should have been the context I read it in.
| quenix wrote:
| 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.
| SillyUsername wrote:
| 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.
| cqqxo4zV46cp wrote:
| What specific issue do you have with this article? "The
| graph itself isn't at fault" is very "guns don't kill
| people, people kill people". Who cares? This distinction is
| utterly meaningless semantics. Why do you feel a need to
| 'stand up' for box plots? Why is this a tribalistic
| religious war?
| JumpCrisscross wrote:
| > _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.
| cqqxo4zV46cp wrote:
| 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.
| sloowm wrote:
| 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.
| munch117 wrote:
| > 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 wrote:
| just use boxplots with an overlay of the actual data and any
| confusion goes away
| flumpcakes wrote:
| 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.
| cjk2 wrote:
| 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...
| ohmyiv wrote:
| > 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.
| cjk2 wrote:
| 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.
| scrollaway wrote:
| Is this sarcasm?
|
| I'm not one to appeal to authority but "author should take
| a course" is akin to ad hominem when a quick look at their
| profile (https://www.practicalreporting.com/about-nick-
| desbarats - https://www.linkedin.com/in/nickdesbarats/)
| tells you that he's been doing dataviz and statistics for a
| long time.
| cjk2 wrote:
| Nope.
|
| I'm not one to appeal to authority either which is why I
| am making objective arguments about what is presented.
|
| And yes he should go on a stats course. I dread to think
| the chaos he's spread to people who don't know better.
| ohmyiv wrote:
| > The author is way out of their depth and should retract
| the article and take a formal, accredited statistics
| course.
|
| Maybe you should learn about the author before you make
| such assumptions. I find it hilarious you think he should
| take statistics courses when he teaches data visualization
| workshops to places like NASA, IRS, and the UN.
|
| I'm done with this thread. Such a joke.
| cjk2 wrote:
| Oh I know the author.
|
| Just because you're high profile in the data viz industry
| doesn't mean you should be commenting on statistics
| especially with such a clear misunderstanding going on.
|
| Some of us are definitely more qualified to speak on
| these matters and we still don't think we're qualified to
| teach it.
| ubercow13 wrote:
| If box plots require an formal and accredited statistics
| course to understand, but as you mention they are taught
| to 15 year olds (presumably incorrectly) in school and
| used by people with power making decisions that affect
| everyone in organisations such as the UN and NASA, then
| even if the author is unqualified it seems their point is
| 'accidentally' correct. No one should be using these
| plots except extremely smart and trained people who do
| know how to read them, as it could have serious negative
| consequences.
| pocketsand wrote:
| I do stats and data viz for a living and the article seemed
| perfectly reasonable to me.
|
| He isn't dogmatic.
|
| He makes reasonable arguments.
|
| I'm confused by these hopelessly uncharitable readings of
| the article.
| sloowm wrote:
| 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 wrote:
| 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.
| cjk2 wrote:
| 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!
| kibwen wrote:
| _> 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._
|
| this is precisely why i don't bother with capitalization in
| my sentences.
|
| in fact even punctuation isnt necessary i dont see why i
| should dumb down my explanations for people who arent going
| to make an effort to understand them
|
| actuallyevenspacesaresimplyredundantandasufficientlysmartre
| adershouldjustunderstandmymeaningwithoutmeneedingtodelineat
| emywordswhataretheyachildifthiswasgoodenoughfortheancientro
| mansthenitsgoodenoughforme
|
| hckvnvwlsrrdndntndfnynsysthrwsthnmycnclsnsthtthrbrnsrnsffcn
| tlylrgtcmprhndmygns
| nkrisc wrote:
| Do you want to be right, or do you want to be understood?
|
| You can't control what other people do. You can try to meet
| them where they are, or hope they'll catch up with you.
| Hopefully it's not your problem if they fail to.
| psyklic wrote:
| 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.
| sigmoid10 wrote:
| >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.
| blueflow wrote:
| This should be the topmost comment. Box plots are made for
| visualizing generalized normal distributions and nothing else.
|
| Edited to preempt nitpick.
| cjk2 wrote:
| 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.
| crazygringo wrote:
| But representing skew is precisely to show how "off" from a
| Guassian it is.
|
| Because real data is never perfectly Guassian, or perfectly
| anything.
|
| But the idea of a box plot is that it's for data which is
| _in theory_ Gaussian or a similar unimodal kind of bell-
| shaped curve.
|
| Then you can look at the box plot and see if it actually
| _is_ -- are the two boxes roughly equal-sized? Are the
| lines a bit longer than the boxes but not insanely so?
| blueflow wrote:
| You model skew in Gaussian distributions by adding an
| exponential parameter.
| pocketsand wrote:
| Why? They're non-parametric and make zero assumptions of
| normality.
| blueflow wrote:
| How else would you calculate the quartiles to render the
| boxes?
| munch117 wrote:
| Count data points in each quartile. You can do that for
| any sortable data, independent of distribution.
| blueflow wrote:
| If you do that in your paper, you better write next to
| the graph that you did that.
| thaumasiotes wrote:
| Arguing that nobody who might be professionally expected
| to look at a box plot can be reasonably expected to
| understand how box plots are defined doesn't make a
| compelling case that using them is a good idea.
| blueflow wrote:
| If the method how the plot boxes are calculated is not
| clear (this thread references at least two different
| methods), you'll need to explicitly write it down which
| methods you did use.
| thaumasiotes wrote:
| > this thread references at least two different methods
|
| No, as the sidethread comment notes, there is only one
| way you _can_ compute quartiles. You seem to be arguing
| that the correct thing to do is to impute them, and that
| _calculating_ them is such a deviant practice that it
| would need to be specially remarked on.
| blueflow wrote:
| Isn't this what i was saying from the beginning?
| Box plots are made for visualizing generalized normal
| distributions and nothing else.
|
| And now people in this thread argue you can calculate
| them from something else. Not sure if you are replying to
| the right post.
| thaumasiotes wrote:
| That might be what you were saying from the beginning,
| but the only thing that that would establish is that
| you're completely out of touch with reality. Box plots
| are made for visualizing quartiles.
|
| Your theory would imply, among other things, that the
| median line going through the box part of a box plot
| always divides it in half, which obviously is not the
| case.
| blueflow wrote:
| No? Exponential Gaussian?
|
| Whatever you do, you should explain first what you do
| that your whiskers stay meaningful and are not just
| whatever randomness your outliers produced.
| A4ET8a8uTh0 wrote:
| It is actually a fascinating argument that shows how
| little of what is being decided is based on actual data (
| or at least our understanding of it ), but rather that
| data visualization is being used to push already pre-
| approved decisions with data being used merely as a 'for'
| argument.
|
| I agree that if there is an indication that if most
| professionals don't really know what boxplot is supposed
| communicate, maybe it should not be used.
| munch117 wrote:
| Perhaps I expressed myself poorly, and left room for
| misunderstanding, because I cannot possible imagine that
| we have any real disagreement on how to compute
| quartiles.
|
| Any set of numbers I give you, you can compute quartiles
| for it. There is no algorithm for doing that that breaks
| down if the numbers don't follow a normal distribution.
| blueflow wrote:
| Look at this SVG from wikipedia: https://upload.wikimedia
| .org/wikipedia/commons/1/1a/Boxplot_...
|
| When you calculate the box plot using normal distribution
| parameters, the outliers are outside the outer bracket.
|
| If you split the dataset into 4 equal parts, the bracket
| will be larger because the outliers are still inside it.
|
| The methodologies are not equal.
|
| This thread is the first time i heard people do the
| "split dataset into 4 quarters" and using that for box
| plots.
| pocketsand wrote:
| As I'm sure you know, there are a lot of variations on
| how quantiles are calculated in various software. The
| 25th percentile, e.g., doesn't always line up with a
| value in the dataset, so sometimes nearest rank methods
| are used, otherwise a linearly interpolated data point,
| where interpolation is done in various ways.
|
| In any event, none of these methods assume normality, or
| rely on CDFs of a normal curve.
|
| If they did, every box plot would be symmetric.
|
| The fact some people think that boxplots are constructed
| in such a way is a pretty good reason to take the
| author's article seriously as for how boxplots are
| confusing.
| ColFrancis wrote:
| For what it's worth, you've convinced me that my beloved
| box plots need to be explained if I want to use them
| again.
|
| The SVG you've provided clearly shows that the box plot
| splits the data in 4. The interquartile range (IQR) is
| clearly marked and it even has a comparison for what the
| standard deviation (variance) measure would be.
|
| Secondly, if the data truly came from a normal
| distribution, there are no outliers. Outliers are data
| points which cannot be explained by the model and need to
| be removed. Unless you have a good reason to exclude the
| data points they should be included. This is why I like
| the IQR and the median, they are not swayed by a few wide
| valued data points. The 1.5*IQR rejection filter I think
| is lazy and unjustified. Happy to discuss this point
| further as it is a bug bear of mine.
| blueflow wrote:
| When i said "splitting", i meant it like my parent
| explained: Basically sorting your datasets and then
| splitting into quarters.
|
| What you want to explain to me (IMHO to the wrong person)
| is the correct approach of calculating a mean and
| standard deviation and drawing the box from that. Lets
| stay with that (and thats what i said earlier in the
| thread)
|
| After i wrote the post you replied to, i realized that
| the pure "splitting" method for box plots is nonsensical
| since the outer brackets interval is determined by the
| two most extreme values. They are too random to be
| meaningful. It does not make sense to draw a box plot
| from that.
| blueflow wrote:
| On second thought, this method makes the outer brackets /
| whiskers pretty much useless since their position is
| determined by the largest outliers, which is quite much
| random.
| Falkon1313 wrote:
| That's not how they're drawn. Outliers (More than 1.5
| times the interquartile range outside the 1st/3rd
| quartile) are plotted as dots beyond the whiskers. The
| whiskers go at Q1-1.5xIQR and Q3+1.5xIQR.
| blueflow wrote:
| Better is! Look what i was replying to.
| munch117 wrote:
| 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?
| cjk2 wrote:
| 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.
| munch117 wrote:
| 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?
| cjk2 wrote:
| Some of us are perfectly qualified to understand them and
| the nuances.
| munch117 wrote:
| A plot that requires the reader to be perfectly qualified
| is a bad plot.
| cjk2 wrote:
| They teach this to 15 year olds in the UK.
|
| If it's a bad plot, perhaps some introspection is
| required...
| magicalist wrote:
| They also teach pie charts and use color scales with non-
| uniform brightness. Just because it's possible to read a
| plot doesn't make it a good plot.
| Beldin wrote:
| > 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]_
| sigmoid10 wrote:
| 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.
| rcxdude wrote:
| The mean and variance are not features of a box plot. Box
| plots show the quartiles, which are about the cumulative
| distribution.
| lolc wrote:
| Which is why I find the article so compelling because I'd
| always read box plots as being about variance. To me the
| plot implied a quite normal distribution.
| fjkdlsjflkds wrote:
| Note that "not knowing how to correctly interpret a
| boxplot" is not equivalent to "boxplots are useless".
| lolc wrote:
| If people like me are in the audience, they might be
| worse than useless.
| fjkdlsjflkds wrote:
| Sure. But if someone is using, for example, a notched
| boxplot to quickly evaluate differences in medians (i.e.,
| they know how to correctly interpret a boxplot), it can
| still be a useful plot that conveys specific information
| that you would otherwise not get when looking at a violin
| plot, histogram, kernel density estimate or a strip plot.
|
| My point, again, was: just because a boxplot is not
| useful to _some_ people, doesn 't mean that it is not a
| useful plot (particularly when augmented with a rugplot
| or a strip plot). Plots are not just used to convey
| information to _others_ : they are also a useful tool in
| exploratory data analysis.
|
| Notice that you can also apply the same critique to
| almost any plot: _some_ people don 't know how to
| interpret a violin plot (or kernel density estimate plot)
| correctly... does that make them useless?
|
| The main advantage of a boxplot is that it is parameter-
| free (unlike histograms, violin plots and kernel density
| plots) and quickly conveys very specific information
| (median, range, quantiles, confidence interval for the
| median) that other types of plot usually don't.
| These335 wrote:
| Mean and variance have nothing to do with boxplots, you are
| mistaken.
| JumpCrisscross wrote:
| > _For a box plot you need to calculate mean, variance_
|
| Quantiles and medians. (Plus min and max.) Non-parametric.
| Evidlo wrote:
| > So you can no longer reverse the process
|
| I've never understood this to be the purpose of a boxplot,
| only a means of visualizing a distribution's quartiles.
|
| You've gotten a flood of comments from upset people, so
| I'll keep it short by saying that a boxplot doesn't
| actually do what you claim for Gaussians, as the 0 and 100
| percentile "whiskers" would be at plus/minus infinity. As
| for a bounded bell-shaped distribution, there are several
| non-unique ways to define such a distribution.
| crazygringo wrote:
| > _as the 0 and 100 percentile "whiskers" would be at
| plus/minus infinity_
|
| The point is not to plot an ideal Gaussian, the point is
| to plot the data.
|
| In real life the whiskers are the actual minimum and
| maximum values observed.
| blueflow wrote:
| > In real life the whiskers are the actual minimum and
| maximum values observed.
|
| Look at this: https://upload.wikimedia.org/wikipedia/comm
| ons/1/1a/Boxplot_...
|
| 0.7% of all values are outside the whiskers.
| crazygringo wrote:
| There are two standard ways of doing box plots. One is
| miniums and maximums, the other is the 1.5 IQR method.
|
| The very Wikipedia article your image comes from explains
| this:
|
| https://en.wikipedia.org/wiki/Box_plot#Whiskers
| gradstudent wrote:
| > because these values unambiguously define such a shape
|
| I think this is a misunderstanding, and I think it is
| shared by the author of the article. Boxpolots show ranges.
| That's it.
| treflop wrote:
| 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.
| rcxdude wrote:
| 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?
| cjk2 wrote:
| Because it's very hard to rationally compare multimodal
| batches without single test statistics. And they present
| five summary figures for each batch, each of which are
| reasonable metrics to compare batches with.
| treflop wrote:
| Well usually when you are analyzing some data, you toss it
| into the most basic chart like a histogram.
|
| And a histogram for the author's example is perfectly
| acceptable to show that _single_ data series.
|
| But imagine if you have 10 different normal data series and
| you want to compare their medians and distributions between
| each other... well are you going to put 10 histograms side
| by side and expect the reader to compare them? No -- that's
| where the box and whisker plot shines.
| smcin wrote:
| Simple, use a histogram.
|
| The author's first histogram clearly shows most of the
| distribution lies in [20,100), then the [10,20) bin is
| empty but the [0,10) bin is quite full. Hence, that's not a
| single-mode distribution. It has two modes, one around
| [50,60) and the other in [0,10).
| WWWWH wrote:
| Yes, exactly! Just plot all the bloody data and be done
| with it. No one is doing this by hand anymore so it is no
| extra work.
|
| To my mind, if you have a genuine EDA attitude you plot it
| all.
| crazygringo wrote:
| > _Just plot all the bloody data and be done with it_
|
| Well no, because you can compare the datasets by eye and
| say questionable _qualitative_ things about them, but you
| can 't make definitively true _quantitative_ statements
| about them.
|
| Show me two plots of data points and I can show you two
| people who will in good faith argue over which one has
| the higher mean or higher median or higher variance.
| Because you often can't tell.
|
| The entire point of something like a box plot is that it
| does part of the quantitative analysis for you. You can
| see where the median is. You can see the width of the
| quartiles.
| theamk wrote:
| But there are much better ways to do this than box plots!
| Lots of CS papers use CDF and it's great and very
| informative once you get used to it (although you do need
| to get used to them). You can have violin plots with all
| the box plots elements and more. Even if you want to
| restrict yourself to quartiles, author's design concepts
| with narrow/wide bars makes much more visual sense, and
| still convey exactly the same information as box plots.
| crazygringo wrote:
| It depends on the purpose.
|
| CDF plots are great for plotting a single distributions,
| but contain way too much information if you want to plot
| 6 distributions next to each other for easy comparison.
|
| Violin plots are interesting but also quite complicated,
| since you have to arbitrarily choose a kernel shape and
| this artificial smoothing can make it look like you have
| much more data than you really do.
|
| I really don't like the author's "alternative designs"
| because I think they're even more open to
| misinterpretation than box plots. It's hard to judge
| though, because the central problem is that the author is
| trying to represent a bimodal distribution, and shouldn't
| be using box plots _or_ the 2 "alternative designs" for
| that.
| thaumasiotes wrote:
| > 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 wrote:
| > 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?
| ohmyiv wrote:
| > 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)
| Ringz wrote:
| From the article:
|
| ,,So, no, I can't think of any situations when a box plot
| would be the truly best choice, other than those in which
| the audience demands box plots because that's what they're
| used to seeing. If you can think of any such situations,
| though, please let me know on LinkedIn or Twitter."
|
| ,,Other reviewers suggested that the conclusion 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."
| cjk2 wrote:
| Comparing location, spread and skew of multiple batches.
| rzmmm wrote:
| 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.
| oefrha wrote:
| The alternative plots in TFA after
|
| > Design concepts such as the ones below make more 'visual
| sense' than box plots:
|
| present the exact same info in much less visually confusing
| ways, through the use of brightness (weight) and area. Just
| better box plots.
|
| And of course you can always draw some lines for the
| quartiles on any kind of plot with a linear scale for the
| value.
| ajuc wrote:
| If you want quantititive information it's better to use a
| table anyway - precisely because it doesn't mislead you
| about the internal distribution.
| rcxdude wrote:
| 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 wrote:
| > 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.
| conformist wrote:
| In the mathematical sense this is clearly not true - it's
| easy to come up with a smooth single mode distribution that
| doesn't look like a bell.
| thaumasiotes wrote:
| Is it? How?
|
| You could include a lot of little bells far from the
| single mode, but that's reading a little too much into
| the literal meaning of "single mode" - a "bimodal"
| distribution isn't one where the two most common values
| are both modes. It's one where there are two distinct
| local maxima.
|
| The tails to the left and right must asymptotically
| approach zero (or you don't have a smooth distribution,
| because you have discontinuities somewhere), and if
| there's just one local maximum, your curve will look like
| a bell.
| commathingy wrote:
| The exponential distribution (modal value 0) is not bell
| shaped. If you don't like it's range of non-negative,
| then take some smooth mollification
| thaumasiotes wrote:
| And the smooth mollification will look like...?
| pxx wrote:
| in the simplest case... just mirror it (some call this a
| Laplace distribution). if you don't like how it's not
| differentiable at the mode there are further smoothings
| (see, e.g., the wikipedia article for this distribution)
| but this simple construction is continuous.
| canjobear wrote:
| It looks like a spike, not a bell.
| cycomanic wrote:
| A spike is not smooth (typically meaning continuous in
| the variable and its first derivative), which was one of
| the conditions.
| timy2shoes wrote:
| Then take a Cauchy or a t-distribution. Basically
| anything with a longer tail than exp(x^2). The Gaussian
| summary will be misleading because of the tails.
| seanhunter wrote:
| Yes or the chi-square distribution with k=1 or 2 or any
| other of the gamma distributions[1] with the right
| parameters will have a shape that is one-sided with the
| mode at the lower extreme and no "low tail" in the normal
| sense.
|
| [1] https://en.wikipedia.org/wiki/Gamma_distribution or h
| ttps://stats.libretexts.org/Bookshelves/Probability_Theor
| y/...
| davidguetta wrote:
| The bell curve IS the smooth single mode with LOWEST
| ENTROPY.
| nequo wrote:
| Do you mean greatest entropy? Not if the support is, for
| example, the positive reals.
| kazinator wrote:
| 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.
| seanhunter wrote:
| > The only way to use it is to recover the five parameters
| from it, and then stop looking at it.
|
| ...which is its intended use case since Tukey invented it
| as a way of visualising the "5 number summary". I think
| part of his criteria were that it should be easy to make by
| hand which is clearly no longer a consideration so there
| are plenty of reasons to just do something else most of the
| time these days.
| wesleywt wrote:
| This is exactly why the author says you should stop using Box
| plots. The plot is easy to misinterpret.
| IanCal wrote:
| > 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 wrote:
| 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.
| afiori wrote:
| Quartiles are relevant for almost any distribution
| crazygringo wrote:
| If by "almost any" you mean "unimodal".
|
| Quartiles are not relevant, i.e. can be highly
| misleading, for a bimodal distribution or beyond...
| afiori wrote:
| they are misleading if you assume unimodality, but are
| always relevant. If you care about how many modes there
| are then likely you would prefer deciles or centiles.
|
| But even in the first image of the article the fact that
| two quartiles are close together means that there some
| density peak around there.
|
| I agree with the author that box plots are not good
| plots, but quartiles/deciles/medians are useful even for
| multimodal distributions
| amelius wrote:
| A bell shape has no minimum/maximum, like the box has.
| Hendrikto wrote:
| In theory. In pratice you always have a finite sample size
| and thus a min and max.
| lkdfjlkdfjlg wrote:
| Boxplots don't assume anything about your data. They just
| measure percentiles and put them on the y-axis.
| vehemenz wrote:
| 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.
| jncfhnb wrote:
| > 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
| fnordpiglet wrote:
| 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.
| sigmoid10 wrote:
| 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.
| jncfhnb wrote:
| The GP here did not claim a poisson was the same thing as a
| Gaussian. They also don't look similar.
|
| As far as I can tell you're making the introductory student
| error of thinking the central limit theorem means any
| sufficiently large sample makes a distribution look normal.
| crazygringo wrote:
| 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 wrote:
| 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 wrote:
| Murphy's law for data viz:
|
| If a plot _can_ mislead, it _will_.
| theamk wrote:
| 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 wrote:
| 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_ wrote:
| 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
| sigmoid10 wrote:
| >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 wrote:
| 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 wrote:
| > 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.
| mkl wrote:
| 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 wrote:
| 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.
| mkl wrote:
| Violin plots essentially are KDE plots, but you can put
| multiple of them on the same axes to compare groups.
| medstrom wrote:
| 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
| catlifeonmars wrote:
| For the latter, it looks like the histogram is probably
| sufficient. The violin plot just adds extra visual noise.
| klysm wrote:
| A violin plot is literally just a KDE sideways.
| seanhunter wrote:
| It also has a box plot tacked on because "why not"?
| jhbadger wrote:
| 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 wrote:
| 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
| sanderjd wrote:
| 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?
| interroboink wrote:
| Her criticisms of violin plots seem to be (1) they combine
| histogram-style information with box-plot-style
| information, when you generally would only want one or the
| other [ie: don't use boxplot for bimodal, don't use
| histogram when boxplot suffices], (2) The histogram-style
| information is not comparable between blobs of data, since
| they're not visually aligned, have no tick marks, etc -- a
| plain histogram is better for this, and (3) she finds them
| ugly on a personal level.
|
| EDIT: Maybe she'd be fine with using them in an exploratory
| manner. She seems to mainly be complaining about using them
| in publications, meant for other people to consume. Also: I
| did not watch the entire video (:
| sanderjd wrote:
| Thanks for this summary! I definitely hadn't seen the
| point about comparability between blobs of data because
| of the alignment. But that really seems like an odd point
| to me, as I almost entirely see / use these with time
| series data, where pretty much the whole point is to
| compare the evolution of the values over time using their
| "vertical" location, with a was to see the shape of a
| distribution of values at each point in time, at a
| glance.
| SebastianKra wrote:
| Her argument that convinced me, is that the same result can
| always be better represented with multiple histograms -
| z-stacked, side-by-side, 3D or ridgeline-plots (ridgeline
| plots look awesome). Check out her examples at 21:11.
|
| Compared to these alternatives, violin plots are comically
| bad.
| Aachen wrote:
| The two other replies are her main point(s), but the video
| also spends some time on another issue that she labels as
| minor but I found interesting to hear the perspective on.
| I'll try to do it justice:
|
| They look like vulvas. We're all adults, it's not a problem
| typically, but given that it's an aesthetic choice
| (noticing how half of the chart conveys the same info
| without this property), why? And it does come up, like if
| someone does make a joke about it, a room full of typically
| only well-meaning men will now look to her if she's
| comfortable with the joke and, what was okay before, now
| turns into a feeling of being singled out and outside the
| rest of the group
| seanhunter wrote:
| The summary is she is saying you almost always want to show
| one of two things (and not both):
|
| 1) To show the distribution, in which case just the
| histogram arranged horizontally in the traditional fashion
| is far better than a violin plot with 2 copies of the
| histogram vertically and some extra quartile stuff tacked
| on, especially since lots of standard libraries to do
| violin plots do kde with very extreme smoothing so the
| distribution they show can be very misleading as to the
| real empirical distribution.
|
| 2) To highlight the summary statistics (quartiles and
| median) in which case just the boxplot is better because
| generally these are hard to read on a violin plot
|
| In case #1 this is usually because the distribution differs
| significantly from a Gaussian in some interesting way that
| would make a boxplot irrelevant or misleading. (eg it is
| bimodal or multimodal).
|
| In case #2 this is usually because the distribution is
| Gaussian (or otherwise standard) and you want to compare it
| with other standard distributions. You don't need all the
| information in the histogram and to include it all would
| obscure the important point(s) you're trying to make about
| the median and quartiles. What is considered standard is
| going to depend a lot on the domain, audience and subject
| matter. In her case, she's an astrophysicist, so if you're
| looking at say red shift data from some observation, other
| astrophysicists will know the distribution you would expect
| to get from that sort of observation for example.
|
| That video is basically a summary of all the conversation
| attached to this article in some ways.
| hoseja wrote:
| 3) They look like THAT
| seanhunter wrote:
| Well yes.
| klysm wrote:
| 100% on the money. Box plots are an archaic technique for
| working around limitations that no longer exist.
| y42 wrote:
| In short and unsurprisingly: Not every analysis and data set
| works with every visualisation.
| montebicyclelo wrote:
| 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.
| scrollaway wrote:
| 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.
| SillyUsername wrote:
| I'm qualified in maths related computing and statistics to
| exam invigilator level, if that helps offset your bias.
| scrollaway wrote:
| No bias -- By commenting a lot, you're overrepresenting the
| average HN audience. Which kind of nullifies your point,
| doesn't it?
|
| You argue in other comments that it's just an education
| problem, but box plots are used with people who don't have
| this exact education you mention, _and_ the article
| explains that a drawback of box plots is exactly that it
| isn 't intuitive and takes several minutes of explanations.
|
| In other words, the article says "I've stopped using this
| because they require education", and your retort is "Don't
| stop using these, you just need to educate people".
| sloowm wrote:
| That background would make you explicitly unqualified to
| asses the quality of box plots as a visualization method.
| Box plots are used throughout various fields of research
| that are far less mathematical in nature.
| SillyUsername wrote:
| Rubbish. They're used extensively in probability
| statistics and confidence intervals. Field of research
| has bugger all to do with it :tears:
| sloowm wrote:
| You not understanding what my comment means is incredibly
| thematic.
| SillyUsername wrote:
| 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 wrote:
| 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
| These335 wrote:
| 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.
| cqqxo4zV46cp wrote:
| 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.
| sloowm wrote:
| 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 wrote:
| 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 wrote:
| 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?
| amelius wrote:
| https://matplotlib.org/stable/plot_types/index
|
| https://d3-graph-gallery.com/
| cb321 wrote:
| 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 wrote:
| And no mention of notched box plots which make a lot of the
| troublesome aspects go away?
| kkfx wrote:
| 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.
| karmakaze wrote:
| > 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
| cb321 wrote:
| 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 wrote:
| 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...
| iainmerrick wrote:
| 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?
| lkdfjlkdfjlg wrote:
| > 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 wrote:
| I can't see why a jitter plot with dark lines marking the
| quartile wouldn't be strictly better for this.
| aniviacat wrote:
| That's just a box plot with extra steps.
|
| Sure, the jitter plot provides more data, but if you only
| make use of the quartiles anyway, that extra data is but an
| unnecessary distraction.
| weebull wrote:
| If you're only comparing medians, then just plot the medians.
| Why a box plot with the quartiles?
| lkdfjlkdfjlg wrote:
| Ok, so the difference between medians is 42.7. Is that a
| lot or a little?
| DonsDiscountGas wrote:
| 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 wrote:
| 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.
| parpfish wrote:
| Overlaid histos can be confusing because people don't know
| if they are stacked or overlapped.
|
| One solution is to smooth into a kde and then use
| transparency to indicate overlap, but that's introducing
| more complexity than you want for a quick n dirty first
| pass
| weebull wrote:
| > 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 wrote:
| 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.
| iainmerrick wrote:
| That's a good explanation, thank you!
| callalex wrote:
| Who drew those boundaries at 15/85? What makes those
| boundaries useful or correct?
| s1artibartfast wrote:
| It sounds like they are business relevant parameters. They
| are self selected and independent of the data or
| distribution.
|
| The point is that they are parameters of relevance to
| observer.
|
| I work in medicine sometimes work with box-plots for this
| reason. The questions "what is the 25th percentile outcome"
| is perfectly legitimate
| s1artibartfast wrote:
| >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 wrote:
| 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 wrote:
| 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 wrote:
| been in love with violin plots
| chefandy wrote:
| 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 wrote:
| 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.
| klysm wrote:
| 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.
| jncfhnb wrote:
| 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 wrote:
| Importantly, box plots are also ugly. Beauty matters.
| Falkon1313 wrote:
| 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 wrote:
| ECDF plots are what I use.
| __mharrison__ wrote:
| 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 wrote:
| I'm probably wrong, but this entire article felt as an
| advertisement for violin plots without it being mentioned once
| pvaldes wrote:
| 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 wrote:
| Plotly has an option on box plots that shows the individual
| points as well, which I like better than violins
| greentxt wrote:
| Just use a heat map instead. /s
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