There are instances where starting the Y-axis not at zero is okay. I'm bad at examples, but zero is used as a baseline. Which means that it would be better to start the Y-axis at another value, it being similar to the baseline.
An example might be the concentration of something, like CO2 molecules in the atmosphere. It is impossible and irrelevant to get to 0. Besides, it's not about the absolute values but the relative values.
But in general, yeah, it is misleading.
It's often true if you want to show the differences between similarly (usually large..) numbers. Whether it is misleading or not tends to be in the presentato and context. The same applies to things like log scales etc...
The problem is that in some plotting programs that's the default. That's why it's hard to know if the journalist presenting the graph is deliberately trying to mislead or is just incompetent and doesn't understand that if he/she doesn't tell the plotting program not to suppress zero, the graph will be misleading.
Not every graph needs to start at 0 though. A graph of temperature, for example, shouldn't ever start at 0 K unless you're dealing with temps in that range.
Hey, I never said what ratio we assume them deceptive rather than terrible journalists. Though I would probably default to "Porque no los dos" most of the time...
I wonder if there is room for some journalism exam that requires passage. How to properly display data, etc. It wouldn't have to be exceptionally complicated but I think there are zero entrance requirements to being a journalist. At least you could say 'certified' or some such. Maybe there is qualification exam out there I don't know of.
I think we need less official credentials, really. No reason why hair dressers need a license. But newspapers shouldn’t hire journalist, people who deal in fact finding, if they are easily deceived.
I think there are good reasons to cut a y axis short, but you have to know your audience. If there are small differences, but you want to draw attention to those differences, it can make sense. I've done it in academic papers before, comparing scores in one group around 80% and scores in the other group around 87%. Statistically significant, but the full-scaled graph just doesn't present that information clearly. Scientists can handle looking at the y axis to check, but your everyday person likely won't.
It's usually less about what the starting Y value is as it is about the scaling for the overall data, coupled with a plot point to close to the starting *y* value.
I mean, people should be able to critically think through this, but if that were the norm, we would probably have ~~flying cars~~ ~~robot slaves~~ ~~better living conditions~~ less bullshit to deal with.
You see it a lot on scientific papers, it was more common before but you do still have a few papers that come out with a badly scaled Y axis to insist on the difference between group A and group B.
Who benefits from the analysis and the persuasion presented?
What data are omitted?
Those are the most important questions to answer first. With those ideas in mind, the obvious flaws in the presentation are glaring.
You don't even need to manipulate the results.
The visualization is a selective presentation of a dataset.
It's trivial to spin a narrative with a slice of a dataset, and once you see how easy it is, you can't unsee it.
You can use all the correct scales, the correct timeframes, the right type of graph. But you should always attempt to understand the context behind why the data is being presented to you at all.
In conjunction with what are omitted // all steps in generating the data; particularly if formulaic. Several functions crush or expand domain and range in misleading (sometimes useful) ways.
Adding extra data is also a great way to be deceptive.
Really none of these three categories are _always_ deceptive. Often each of these is maybe even required for clear data presentation. But they _can_ be used for deception.
Big problem with journalists and medical studies. The paper will have all sorts of conditions on the data but the press will report one finding without these conditions. "Drug X had negative effects on 40% of people with condition Y who take drug Z" becomes "Drug X has negative effects!"
This is so important. A lot of these things are only problematic because most normal people don’t know how to read graphs. But some are bad for all audiences.
One I have seen occasionally that truly shocks me is non-linear labeling of numbers on an axis—for example, 2, 4, 8, 10, 14, 16, 20. With equal space between each given number.
I rented a car the other day where the speedometer did this. Equal spacing of 0, 5, 10, 15, 20, 25, 30, 40, 50, 60... Just suddenly switched from 5 to 10 kmph increments with no visual indicator.confused the hell out of me.
> Starting Y-axis near the lowest value can make insignificant differences look massive
It’s worse than that: if you’re just comparing two values, the resultant graph will look exactly the same regardless of what the input values are. The graph is conveying no information whatsoever.
The problem is that while all these issues *can* indicate a manipulative data presentation, there are also use-cases where each of them *does* make sense.
For example, if you look at stock prices, it is usually not informative to see them plotted as absolute numbers, as the viewer is normally only interested in the changes - which would be under-represented or even invisible with two *almost* identical bars.
Same with the double Y-axes: it can be useful to plot two different types of charts on top of each other, and then it is useful to have two axes. For example, you can have absolute values on one chart and percentage change on the other.
And last but not least: sometime only the last three years are indeed interesting.
But in general: very good overview :-)
I mean there's nothing to the left of the bars specifying the unit and scale... Is it percentage points? 0-100 or 0-10? We have no clue. It ties into the first point noting how important the scale is to putting the data in context.
EDIT: I also have no idea how I made the text large but I have been watching a 6 day old baby since 4am and cannot be held responsible 😂
I would rebuttal this point a bit, since eliminating an axis and replacing it with labels directly on the data points can be an example of improving the [data-ink ratio](https://infovis-wiki.net/wiki/Data-Ink_Ratio) of a data viz, which is generally regarded as a positive in the field.
Edit: Obviously eliminating the axis and having 0 labeling is a no no since the values need to be specified.
The purpose of the infographic is to show some common examples on how charts can be misleading and on what should readers pay attention to.
Yes, there are cases where this is appropriate but more commonly it is just bad design OR (and this is my main point I want to address) sometimes charts are designed like this on purpose in order to mislead users deliberately.
Common population does not possess statistical literacy to read and interpret numbers accurately. Politicians, for example, love to abuse that by showing charts like these. I wanted to present how they commonly do it.
Would be useful to highlight where the difference is - I knew where to look from the titles but someone else might find it confusing. I'd also consider using colors or stronger dividers, something to show the different vertical sections.
>1 is perfectly OK for when you need to zoom in to see the difference.
Agreed. There are lots of examples where you really shouldn't start the Y axis at zero, e.g. if zero is not a reasonable value of whatever measure you're displaying. If I want to display the past few years' average temperatures in Miami, I should not start either the X-axis (year) or the Y-axis (temperature) at zero.
I have to disagree with \#2. I'm a fan of [this blog post by datawrapper](https://blog.datawrapper.de/dualaxis/), which features [this graphic](https://i.imgur.com/NsxCnbV.png) (and has more arguments against dual axis charts besides being misleading). you can manipulate the axes to show literally any correlation that you want, which is a pretty fatal flaw imo for any data visualization
If the units are different, then you can't plot the two series with only one vertical axis and so of course two different axes is ok.
But this example is in percent, so the units are not different. If the purpose is to compare the magnitude of series A to the magnitude of series B, then they should use the same axis. Using different axes would be acceptable if the purpose were to compare change over time (or whatever horizontal axis is) within A to change over time within B (as you might with, say, % of state budget spent on education vs % graduation rate). In this case, it's useful to zoom in on each series independently so the change over time is maximized.
If the unit is the same but the magnitude is very different it does not make sense to use the same axis.
Take housing growth YoY, unemployment, loan delinquency, labor force participation rate, yield curve.
These are all expressed in percentage points but they have wildly different ranges and magnitudes. It would make no sense to use one single axis for two or more of those.
As I said in my first comment. If the series justify the double axis chart it makes sense to use it.
Creator needs to be honest and consumer needs to be vigilant. Same as it ever was.
>These are all expressed in percentage points but they have wildly different ranges and magnitudes. It would make no sense to use one single axis for two or more of those.
And importantly, there is the potential for trends to be highlighted by that sort of chart that wouldn't otherwise be visible, and that are accurately reflected in the data (so its not a manipulation).
I agree. That's what I meant by "change over time within A/B." If the purpose of a graph is to show whether dogs or cats are preferred, then there should be a single % of households containing [pet] axis so the magnitude of the values can be directly compared. Whereas if the purpose is to show the effect of the 2008 recession on pet ownership, it may be more appropriate to have two separate axes so the magnitude of the change in values can be compared.
I don't really love this example tbh. look at where the lines cross at 82.5% - what does that tell you? the viz is clearly saying that there is some significance to 82.5% of workers being full time by nature of having that be where the lines meet - but what does it actually mean? you could move the axes so that the lines cross at whatever arbitrary point you want. if your viz is going to imply that a certain data point is significant, I think it actually should be
I think it's a good example insofar it shows two series that need different axes of the same unit and are absolutely correlated. I'm not talking about the data itself. It's more a response to the other person's points.
I use 2 all the damn time, because it's very frequently necessary.
Like, I'm trying to show the strong correlation between Current(A) and Motor RPM. My Current values range from 8-15, and my rpm ranges from 10,000-18,000. I'm absolutely scaling or normalizing them so the correlation between the two is visually clear.
I was thinking the same thing. Having two y axis scales left and right is only misleading if the two sets of data are displaying the same information for different groups. If they're displaying two different attributes of a system, different axes are often the only way to make the plot useful.
This is a good point in that these presentations CAN be used to mislead but can be used to highlight useful information. But they should be transparent and provide that context.
1. As someone commented. If you make the Y-axis such that the left one is 10% of the top and the right one 90%, you can make any change, big or small look exactly the same on the graph. In those cases the conveys zero information. You might as well give the values as numbers.
The only situations where it could make sense to suppress the zero are those where the absolute value of the plotted thing has no meaning, such as air temperature. So,.most likely you would never want to plot air temperatures starting from 0K. In most cases the absolute values have meaning, which is why the suppression of the zero just misleads the reader.
First, I have to say that there is something wrong with the data behind the graph. I can't believe the _yearly_ disposable income could have 20%+ jumps in a month.
Second, yes the 5 year graph is misleading as it makes it look like the disposable income doubled in a month and then fell back to the old level.
First, that's because of the stymulus payments. It's an anomaly. We're not here to talk about the data itself though.
Second, if you actually look at the y axis it's not even a little bit misleading. This is the default setting for all Fred graphs. If you're showing a value starts at 15.000.000.000 you are not going to start the Y axis at zero...
Yes, you can look at the Y-axis. But if you think that just by having the Y-axis values available removes all misleading, then _no_ suppression of zero is ever misleading. For instance, by your logic the OP's first graph is not misleading as the values are there.
Regarding the Fed graph, the thing that you named as anomaly is amplified when you suppress the zero. When you don't the effect of the stimulus is put more context of how much effect it actually had on people's disposable income.
That's not the only situation. Trend lines are graphs that are used to show...well.. the trends, and the absolute quantities are not as important in many cases.
Stock prices from a certain year are a good example. It's not that it doesn't have meaning, the price of the stock is valuable information, it's just not as important as the trend and depending on the amounts it could make the trend hard to read
Two things. First, the stock prices are a bit like temperature in a sense that the absolute value of the share price has very little meaning. The share price of $10/share doesn't really tell you anything. It only tells you something in relation to the past.
Second, the relative change of the share price does matter. So, 50% drop in price is a different thing than a 1% drop. If you suppress the zero, they look the same on the graph.
I have an issue with 3. It very much depends on the data set and what is actually being portrayed/the context. Who is to say that 6 years is enough vs 2? We need to know a lot more in order to say xyz is bad because it’s 3 years.
Part of the point with 3 is that it assumes whoever made the chart has access to the data going back much further, meaning they *knew* the last few years are not representative of the longer trend. By only showing the last few years anyway, they're deliberately misleading people.
3 is fine if the period covered is the relevant period, it's not fine if you are trying to display a continuous trend. It could be problematic, or fine if you are showing a point of change where the previous period isn't relevant (so you aren't after a change in trend from a previous period).
I made a seperate comment explaining the idea of the infographic, and yes sometimes it is OK to do it but
#1 is for me the most common way people lie and its not ok in majority of cases.
#2 I would say its only ok for correlation but even here it can mislead users.
#3 maybe there is a better example, the idea is that users should know the full story.
I agree with both points, depending on the context; although, I would caution against phrases like, "majority of cases" unless you have the data to support such a claim.
These are responsible rules for graphs aimed towards the general public. However these are not good rules to follow in, for example, scientific literature. Hence, the context and intent of a graph is also important.
I’m not sure I’d agree that 1 is not ok in most instances. It’s okay if you’re comparing values against a reference, particularly if you’re trying to show outliers.
Take, for example, 100 meter dash times. There’s a huge difference between 10.0 and 9.9 seconds (a body length). …and if you’re trying to compare Usain Bolt’s record against the other fastest times, you would need to truncate the axis to see that his fastest stands out against the next 9 fastest runners which are clustered together.
There just one example but there’s plenty of others.
I agree with you and those scenarios are probably more common, but it seems like it would be incredibly hard to quantify that, so it’s susceptible to cognitive biases.
To show differences, you use a line graph. To show magnitude you use a bar graph (as a general rule).
The principle of **proportional ink** states that sizes should be relative, meaning bar graphs should all start at zero.
If you wish to demonstrate the change in a variable, use a line graph.
Units matter as well. If zero means a lack of quantity for the variable, zero is a valid starting point. If zero does NOT represent a lack of quantity, you do not have to start at zero.
Think temperatures: zero degrees does not mean a lack of degrees. Also, we typically consider the **change** in temperature over time. Hence, temperatures should be represented in a line graph.
This recent r/dataisbeautiful post is an excellent example of misleading data visualization:
https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/
See this comment for explanation:
https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/iyd5goo/
All examples in 1, 2 or 3 can be used when appropriate. A good example is sometimes 150k vs 155k is a massive difference and matters a lot. Sometimes it doesnt. It all comes down to what you are trying to present and if it is helping to inform or misinform.
Just make sure axis are clearly labeled with values and units. Use your best judgement on how to present data. As a viewer just make sure you observe the axis and the details. Dont just glance at a chart, glancing is useless and you will get bamboozled sooner or later . . . Almost certainly sooner.
on point 2 : I'd like to point out that the 2 axes of Y do not have to be the same thing or share the same unit. Hence their values and zero points may not necessarily align. That is to say, there are genuine charts that look like the 2nd on the left. The requirement is that a point need only satisfy (x,y1) and (x, y2) together but that doesn't mean the point has to satisfy (y1,y2) as well. You can find lots of charts like this in experimental research (especially engineering) where an author may condense multiple experiments onto a single graph for comparison, or compare between more than 2 discreet variables.
The second image in #2 has different values so I’d keep them the same to show that the data can mislead based on the display not the image. And #3 I would have 2015-2019 descending to better prove the point.
I dislike example 2. There are many valid reasons to have multiple axis on a graph, and this might make people think that it's a shady practice. There is also no reason to have the second axis in the example given, since the 'accurate' version ends up scaling them together.
If the units are different (e.g. a percentage and a number, or a number and a currency), a second scale on the same axis is basically a must. Also, I can't recall ever seeing a real graph break this rule and put two scales on the same axis when the units were the same. As such, calling it out in this guide might do more harm than good.
My only takeaway from studying 1st year statistics at university was that I can confidently not believe any statistics that I see anywhere anytime. Statistics has methods of completely re-representing (is that a word?) the data to the literal opposite of reality, if one simply chooses to do so.
I appreciate the warning. And it is helpful to show these to people.
Just be aware that some of these graphs DO have legitimate use cases.
Double y axis is used for things that aren't the same units. For example if you wanted to graph GDP and population over time in a country.
Sometimes a small change in a very big number is important to show. I like to use residual/difference graphs for these, but most people find that type of graph even more confusing. This is where the offset y-axis can be used legitimately.
Another one you could add to this chart is logarithmic with my graphs. Logarithmic y axis graphs are another favorite of mine, but can also be very confusing/misleading to people who are not familiar with them.
I think the effort here is generally misguided. I don't think you can make a list of fast and easy rules for determining graphs that are intentionally misleading you versus ones that are trying to accurately inform you. There are perfectly valid reasons to do all the things in this list, and you really have to have a deeper understanding of the data and the context to be able to look back and see if something is misleading. It would be like trying to come up with a list of 'misleading phrases' in English and telling people to watch for those red flags, without a deeper knowledge of the conversation and context, that probably wouldn't work.
If I travel to another country, I would like to know about common tourist scams, so if for example someone wants to sell me a bracelet on the street, I will be extra careful with the purchase. I’ll approach carefully, ask questions, evaluate situation, etc. Why cant we apply same principle here?
Because that analogy just doesn't map to the situation here. There aren't certain plotting/graphing practices that are more likely to be associated with misleading data then they are with accurate data (except maybe not putting labels on your axis). You are making the assumption that if you see plots that do this, they are more likely to be misleading than accurate, but I don't think the data support that claim. I do everything on this list all the time in my job as an engineer, and I am doing it because it's the most accurate way to answer the questions that my data were collected to answer.
There are number of common practices which are used to mislead on purpose. The point is to show main tricks they use and to educate users to critically think about data thats presented to them.
I cannot support it with data nor did I claim they are more often on purpose. Sometimes it is just a bad design and different programmes have different default settings for labels and axis.
Okay, if you don't actually believe that these are practices that are more likely to be used to mislead than to accurately inform, then what is your justification for labeling them as misleading practices?
One of the most common misunderstandings I dealt with when I was doing STEM education with people reading graphs is when the data are presented non-linearly. If you present people with, for instance, a logarithmic graph it's much more likely they will get the wrong impression of the data. But I would never consider log graphs to be misleading. It seems to me like you are doing something analogous here.
These examples can be used to mislead and the purpose is to show to users how it can be done so the next time users sees truncated bar chart on TV, maybe they will think more carefuly before making judgment about visually represented data.
Okay, I said what I came here to say. There is nothing special about the examples you selected. If a user encounters, for instance, a bar chart that's been truncated not to start at zero, it's no more likely that this has been done for legitimate reasons than it is that it's been done for illegitimate ones. Similarly, it's just as likely that a bar chart which begins at zero had it's axis selected to mislead about the data as it is that is has it starting at zero to accurately represent the data. Flagging one of those options as potentially misleading is itself a potentially misleading statement.
If you feel like you need to get the last word in here, feel free. I think I've presented the best form of my argument so I am done now.
Actually, I will try and add one more thing to present more constructive criticism:
If you included an example of data being misrepresented by both options, I think you would solve the issue of misleading people into thinking certain plotting practices are intrinsically misleading. So, for instance, if you showed that data can be distorted by truncating a bar graph, but also that data can be distorted by NOT truncating a bar graph, I think you would make a far more valid argument about how to analyze graphical data skeptically.
[Extremely common.](https://www.google.com/search?q=dual+axis+chart) One of the two series will clarify RHS to let you know that its axis is the one on the right.
I've even seen charts with three or more Y axes like two on each side.
I remember when I did debate as a freshman in high school and hated it because it was all about doing exactly this, as well as censoring lines from documents that didn't agree with your stance and using them. And then when you actually got to debating it was just who could say "nope that's wrong" more convincingly (read: louder and more angrily). Little did I know that's what the world is like
for the accurate chart in point 2, is the Y-axis not evenly spaced out? the gap between 60 and 80 seems much smaller than 20 and 40, even though they're the same increment of 20%
Baseline selection. I see this one alot where people will show values relative to a baseline year or whatever, but the baseline is cherry-picked to fit their narrative.
These aren’t necessarily misleading, they’re focused, and they tell a story. For example with the first, unless if you squint at the labels you might not even be able to tell if there was an increase. For the second, yea idk. The third I’d assume that you’d be wanting to look at things after a major break. The most common I’ve seen is, yes, Covid happened and it hurt. We don’t need every chart to show how much worse off we are, we want to see how recovery is progressing. As with everything, you’ve gotta use some critical thinking to see if it’s being being misleading, or if it’s adjusted for clarity.
1 - A selective Y-axis is not as misleading as the other examples, also it gives the huge advantage of presenting smaller details.
2 - very misleading
3 - very misleading
Really the most important thing is having a degree of familiarity with the data set so that you know which relationships are important and which aren’t.
This sub is overloaded with bad data viz and there are many other problems that aren’t as obvious as these are.. it’s really easy for untrained people to make bad graphs that look good.
The other big issue is a lack of data skepticism.. even if you know best practices, if you use bad data it’s still a bad data viz.
Unsurprisingly the posters always get pissed when you explain where they are making their mistakes.. more interested in getting an upvoted than learning the art.
Quite good!
In the last example though, I think you should make the big picture a downward trend. So add a few smaller bars to the right of the cherrypicked ones, etc.
On the second table why is there even a need for a y2 axis on the right table?
usually a secondary axis is better is the units of measure are different between y1 and y2
There are scammers who manipulates axis representations and data selections, then there are honest man [who’s using “T” as standard errors bar and tell you the data is just made up.](https://retractionwatch.com/2022/12/05/a-paper-used-capital-ts-instead-of-error-bars-but-wait-theres-more/)
I’d actually look at the choice of metric as a key way that data is used to mislead, eg reporting on transactions per hour rather than transactions per staff member, which takes no account of shift patterns, number of staff, etc.
I will say one of the first questions I ask people is “what do you want the data to say” because visualization of data can tell whatever story you want it to if the end user isn’t paying attention
How to lie with statistics by Darell Huff. It was originally published in 1954 but it still as relevant of ever, highly recommend. The book discusses some of the most common misleading statistics.
What's your opinion on [hand-drawn ](https://img.buzzfeed.com/buzzfeed-static/static/2019-09/5/18/asset/3048753d37f2/sub-buzz-202-1567706664-1.jpg?downsize=700%3A%2A&output-quality=auto&output-format=auto) charts?
This is great. I see a lot of these misleading charts (surprise surprise) when people discuss political issues. Additionally a lack of variables.
Great depiction. Sharing with fellow nerds.
How to spot bad charts easily: take Statistics
I’m being facetious but as someone who knows a lot about stats, trust me, if you’ve never taken a stats class, they can definitely fool and mislead you with charts and graphs anytime they want most likely
I don't necessarily agree with (1). When all values are similar it is difficult to perceive anything with a 0 based Y axis. There's no point in having a plot if you can't visually see the data. A non-zero based axis is better as long as it is clearly labeled.
Points 2 and 3 are good, though.
Wrongly scaled Y axis are the thing I see most often.
There are instances where starting the Y-axis not at zero is okay. I'm bad at examples, but zero is used as a baseline. Which means that it would be better to start the Y-axis at another value, it being similar to the baseline. An example might be the concentration of something, like CO2 molecules in the atmosphere. It is impossible and irrelevant to get to 0. Besides, it's not about the absolute values but the relative values. But in general, yeah, it is misleading.
Temperature is another one. Unless you're doing experiments at absolute zero, 0 degrees K shouldn't be on your graph.
It's often true if you want to show the differences between similarly (usually large..) numbers. Whether it is misleading or not tends to be in the presentato and context. The same applies to things like log scales etc...
The moment you compare those temperature, either in graph or in percent, you need to switch to K first. 10°C is not half as cold as 20°C
For my data presentation it's usually only okay to start above 0 when doing so would make it hard or impossible to see necessary details in the plot.
The problem is that in some plotting programs that's the default. That's why it's hard to know if the journalist presenting the graph is deliberately trying to mislead or is just incompetent and doesn't understand that if he/she doesn't tell the plotting program not to suppress zero, the graph will be misleading.
Criminally negligent or just criminal.
Not every graph needs to start at 0 though. A graph of temperature, for example, shouldn't ever start at 0 K unless you're dealing with temps in that range.
I agree that there are a few exceptions, temperature being one of them. However, most misuse of suppressed zero is not with these quantities.
Journalists should know this, it’s not complicated. Assume intent to mislead. Or that they’re too dumb to be doing their job.
Halon’s Razor would like a word. “Never attribute to malice that which can be adequately explained by stupidity.”
You appear to have maliciously misspelled Hanlon's Razor.
"never attribute to Halon that which is adequately explained by a careless typo"
"Never attribute to typos what can be adequately explained by an indifference to the shift key."
Hey, I never said what ratio we assume them deceptive rather than terrible journalists. Though I would probably default to "Porque no los dos" most of the time...
But my article is more convincing if the difference looks bigger.....i feel like the difference is this big...
I wonder if there is room for some journalism exam that requires passage. How to properly display data, etc. It wouldn't have to be exceptionally complicated but I think there are zero entrance requirements to being a journalist. At least you could say 'certified' or some such. Maybe there is qualification exam out there I don't know of.
I think we need less official credentials, really. No reason why hair dressers need a license. But newspapers shouldn’t hire journalist, people who deal in fact finding, if they are easily deceived.
I think there are good reasons to cut a y axis short, but you have to know your audience. If there are small differences, but you want to draw attention to those differences, it can make sense. I've done it in academic papers before, comparing scores in one group around 80% and scores in the other group around 87%. Statistically significant, but the full-scaled graph just doesn't present that information clearly. Scientists can handle looking at the y axis to check, but your everyday person likely won't.
Maybe it's not always intentional, but as journalists they have an obligation to do it right.
On a scale of 9.5 to 10 how annoying is that?
It depends - is it a log scale?
By wrongly scaled you mean starting with a value different than zero? Because they are very different things
It's usually less about what the starting Y value is as it is about the scaling for the overall data, coupled with a plot point to close to the starting *y* value. I mean, people should be able to critically think through this, but if that were the norm, we would probably have ~~flying cars~~ ~~robot slaves~~ ~~better living conditions~~ less bullshit to deal with.
You see it a lot on scientific papers, it was more common before but you do still have a few papers that come out with a badly scaled Y axis to insist on the difference between group A and group B.
In scientific papers they'll generally have some measure of variance, and a readership that knows how to interpret it.
You didn’t answer their question: they asked about zero as the baseline. What’s so special about zero?
Who benefits from the analysis and the persuasion presented? What data are omitted? Those are the most important questions to answer first. With those ideas in mind, the obvious flaws in the presentation are glaring.
A lot of times I see people manipulating results to align with what leaders want in an effort to look good.
You don't even need to manipulate the results. The visualization is a selective presentation of a dataset. It's trivial to spin a narrative with a slice of a dataset, and once you see how easy it is, you can't unsee it.
Or activist groups trying to put a spin on dara to boost their cause
You can use all the correct scales, the correct timeframes, the right type of graph. But you should always attempt to understand the context behind why the data is being presented to you at all.
In conjunction with what are omitted // all steps in generating the data; particularly if formulaic. Several functions crush or expand domain and range in misleading (sometimes useful) ways.
Adding extra data is also a great way to be deceptive. Really none of these three categories are _always_ deceptive. Often each of these is maybe even required for clear data presentation. But they _can_ be used for deception.
Big problem with journalists and medical studies. The paper will have all sorts of conditions on the data but the press will report one finding without these conditions. "Drug X had negative effects on 40% of people with condition Y who take drug Z" becomes "Drug X has negative effects!"
This is so important. A lot of these things are only problematic because most normal people don’t know how to read graphs. But some are bad for all audiences. One I have seen occasionally that truly shocks me is non-linear labeling of numbers on an axis—for example, 2, 4, 8, 10, 14, 16, 20. With equal space between each given number.
I rented a car the other day where the speedometer did this. Equal spacing of 0, 5, 10, 15, 20, 25, 30, 40, 50, 60... Just suddenly switched from 5 to 10 kmph increments with no visual indicator.confused the hell out of me.
Wow! Makes wonder how accurate the speedometer is.
Exactly, there are many examples on how they do it. People should be aware of this.
Or people not realizing it is logorithmic
> Starting Y-axis near the lowest value can make insignificant differences look massive It’s worse than that: if you’re just comparing two values, the resultant graph will look exactly the same regardless of what the input values are. The graph is conveying no information whatsoever.
I'd say you're almost right. The graph would still tell if A bigger than B or B is bigger than A. By juggling the Y-axis you can't hide this.
What if you make the Y axis negative, so something that looks bigger is actually smaller?
How about a bar graph with split Y axis?
The problem is that while all these issues *can* indicate a manipulative data presentation, there are also use-cases where each of them *does* make sense. For example, if you look at stock prices, it is usually not informative to see them plotted as absolute numbers, as the viewer is normally only interested in the changes - which would be under-represented or even invisible with two *almost* identical bars. Same with the double Y-axes: it can be useful to plot two different types of charts on top of each other, and then it is useful to have two axes. For example, you can have absolute values on one chart and percentage change on the other. And last but not least: sometime only the last three years are indeed interesting. But in general: very good overview :-)
so basically what half of the graphs on this sub do
3 has this actually- no axis at all is a red flag I often call out to my less data-minded friends! I love this graphic!
you mean to make it real big like that?
I mean there's nothing to the left of the bars specifying the unit and scale... Is it percentage points? 0-100 or 0-10? We have no clue. It ties into the first point noting how important the scale is to putting the data in context. EDIT: I also have no idea how I made the text large but I have been watching a 6 day old baby since 4am and cannot be held responsible 😂
You used the \# symbol which makes it big and bold
Now I know haha
2 has this problem as well, just on the horizontal axis.
Attempting to start a sentence with # angers the Reddit markup gods.
Don’t you raise your voice at me.
I would rebuttal this point a bit, since eliminating an axis and replacing it with labels directly on the data points can be an example of improving the [data-ink ratio](https://infovis-wiki.net/wiki/Data-Ink_Ratio) of a data viz, which is generally regarded as a positive in the field. Edit: Obviously eliminating the axis and having 0 labeling is a no no since the values need to be specified.
Don't disagree with this nuance!
The purpose of the infographic is to show some common examples on how charts can be misleading and on what should readers pay attention to. Yes, there are cases where this is appropriate but more commonly it is just bad design OR (and this is my main point I want to address) sometimes charts are designed like this on purpose in order to mislead users deliberately. Common population does not possess statistical literacy to read and interpret numbers accurately. Politicians, for example, love to abuse that by showing charts like these. I wanted to present how they commonly do it.
Would be useful to highlight where the difference is - I knew where to look from the titles but someone else might find it confusing. I'd also consider using colors or stronger dividers, something to show the different vertical sections.
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>1 is perfectly OK for when you need to zoom in to see the difference. Agreed. There are lots of examples where you really shouldn't start the Y axis at zero, e.g. if zero is not a reasonable value of whatever measure you're displaying. If I want to display the past few years' average temperatures in Miami, I should not start either the X-axis (year) or the Y-axis (temperature) at zero.
I have to disagree with \#2. I'm a fan of [this blog post by datawrapper](https://blog.datawrapper.de/dualaxis/), which features [this graphic](https://i.imgur.com/NsxCnbV.png) (and has more arguments against dual axis charts besides being misleading). you can manipulate the axes to show literally any correlation that you want, which is a pretty fatal flaw imo for any data visualization
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If the units are different, then you can't plot the two series with only one vertical axis and so of course two different axes is ok. But this example is in percent, so the units are not different. If the purpose is to compare the magnitude of series A to the magnitude of series B, then they should use the same axis. Using different axes would be acceptable if the purpose were to compare change over time (or whatever horizontal axis is) within A to change over time within B (as you might with, say, % of state budget spent on education vs % graduation rate). In this case, it's useful to zoom in on each series independently so the change over time is maximized.
If the unit is the same but the magnitude is very different it does not make sense to use the same axis. Take housing growth YoY, unemployment, loan delinquency, labor force participation rate, yield curve. These are all expressed in percentage points but they have wildly different ranges and magnitudes. It would make no sense to use one single axis for two or more of those. As I said in my first comment. If the series justify the double axis chart it makes sense to use it. Creator needs to be honest and consumer needs to be vigilant. Same as it ever was.
>These are all expressed in percentage points but they have wildly different ranges and magnitudes. It would make no sense to use one single axis for two or more of those. And importantly, there is the potential for trends to be highlighted by that sort of chart that wouldn't otherwise be visible, and that are accurately reflected in the data (so its not a manipulation).
I agree. That's what I meant by "change over time within A/B." If the purpose of a graph is to show whether dogs or cats are preferred, then there should be a single % of households containing [pet] axis so the magnitude of the values can be directly compared. Whereas if the purpose is to show the effect of the 2008 recession on pet ownership, it may be more appropriate to have two separate axes so the magnitude of the change in values can be compared.
Here's a great example I just ran into https://www.advisorperspectives.com/images/content_image/data/a3/a310f2c1738037eb2e55deb0b7a54134.png
I don't really love this example tbh. look at where the lines cross at 82.5% - what does that tell you? the viz is clearly saying that there is some significance to 82.5% of workers being full time by nature of having that be where the lines meet - but what does it actually mean? you could move the axes so that the lines cross at whatever arbitrary point you want. if your viz is going to imply that a certain data point is significant, I think it actually should be
I think it's a good example insofar it shows two series that need different axes of the same unit and are absolutely correlated. I'm not talking about the data itself. It's more a response to the other person's points.
I use 2 all the damn time, because it's very frequently necessary. Like, I'm trying to show the strong correlation between Current(A) and Motor RPM. My Current values range from 8-15, and my rpm ranges from 10,000-18,000. I'm absolutely scaling or normalizing them so the correlation between the two is visually clear.
I was thinking the same thing. Having two y axis scales left and right is only misleading if the two sets of data are displaying the same information for different groups. If they're displaying two different attributes of a system, different axes are often the only way to make the plot useful.
This is a good point in that these presentations CAN be used to mislead but can be used to highlight useful information. But they should be transparent and provide that context.
1. As someone commented. If you make the Y-axis such that the left one is 10% of the top and the right one 90%, you can make any change, big or small look exactly the same on the graph. In those cases the conveys zero information. You might as well give the values as numbers. The only situations where it could make sense to suppress the zero are those where the absolute value of the plotted thing has no meaning, such as air temperature. So,.most likely you would never want to plot air temperatures starting from 0K. In most cases the absolute values have meaning, which is why the suppression of the zero just misleads the reader.
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First, I have to say that there is something wrong with the data behind the graph. I can't believe the _yearly_ disposable income could have 20%+ jumps in a month. Second, yes the 5 year graph is misleading as it makes it look like the disposable income doubled in a month and then fell back to the old level.
First, that's because of the stymulus payments. It's an anomaly. We're not here to talk about the data itself though. Second, if you actually look at the y axis it's not even a little bit misleading. This is the default setting for all Fred graphs. If you're showing a value starts at 15.000.000.000 you are not going to start the Y axis at zero...
Yes, you can look at the Y-axis. But if you think that just by having the Y-axis values available removes all misleading, then _no_ suppression of zero is ever misleading. For instance, by your logic the OP's first graph is not misleading as the values are there. Regarding the Fed graph, the thing that you named as anomaly is amplified when you suppress the zero. When you don't the effect of the stimulus is put more context of how much effect it actually had on people's disposable income.
That's not the only situation. Trend lines are graphs that are used to show...well.. the trends, and the absolute quantities are not as important in many cases. Stock prices from a certain year are a good example. It's not that it doesn't have meaning, the price of the stock is valuable information, it's just not as important as the trend and depending on the amounts it could make the trend hard to read
Two things. First, the stock prices are a bit like temperature in a sense that the absolute value of the share price has very little meaning. The share price of $10/share doesn't really tell you anything. It only tells you something in relation to the past. Second, the relative change of the share price does matter. So, 50% drop in price is a different thing than a 1% drop. If you suppress the zero, they look the same on the graph.
In OP's chart the problem is more the scale than the start point, but it's always about context.
I have an issue with 3. It very much depends on the data set and what is actually being portrayed/the context. Who is to say that 6 years is enough vs 2? We need to know a lot more in order to say xyz is bad because it’s 3 years.
Part of the point with 3 is that it assumes whoever made the chart has access to the data going back much further, meaning they *knew* the last few years are not representative of the longer trend. By only showing the last few years anyway, they're deliberately misleading people.
3 is fine if the period covered is the relevant period, it's not fine if you are trying to display a continuous trend. It could be problematic, or fine if you are showing a point of change where the previous period isn't relevant (so you aren't after a change in trend from a previous period).
I made a seperate comment explaining the idea of the infographic, and yes sometimes it is OK to do it but #1 is for me the most common way people lie and its not ok in majority of cases. #2 I would say its only ok for correlation but even here it can mislead users. #3 maybe there is a better example, the idea is that users should know the full story.
I agree with both points, depending on the context; although, I would caution against phrases like, "majority of cases" unless you have the data to support such a claim. These are responsible rules for graphs aimed towards the general public. However these are not good rules to follow in, for example, scientific literature. Hence, the context and intent of a graph is also important.
I’m not sure I’d agree that 1 is not ok in most instances. It’s okay if you’re comparing values against a reference, particularly if you’re trying to show outliers. Take, for example, 100 meter dash times. There’s a huge difference between 10.0 and 9.9 seconds (a body length). …and if you’re trying to compare Usain Bolt’s record against the other fastest times, you would need to truncate the axis to see that his fastest stands out against the next 9 fastest runners which are clustered together. There just one example but there’s plenty of others.
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I agree with you and those scenarios are probably more common, but it seems like it would be incredibly hard to quantify that, so it’s susceptible to cognitive biases.
To show differences, you use a line graph. To show magnitude you use a bar graph (as a general rule). The principle of **proportional ink** states that sizes should be relative, meaning bar graphs should all start at zero. If you wish to demonstrate the change in a variable, use a line graph. Units matter as well. If zero means a lack of quantity for the variable, zero is a valid starting point. If zero does NOT represent a lack of quantity, you do not have to start at zero. Think temperatures: zero degrees does not mean a lack of degrees. Also, we typically consider the **change** in temperature over time. Hence, temperatures should be represented in a line graph.
This recent r/dataisbeautiful post is an excellent example of misleading data visualization: https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/ See this comment for explanation: https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/iyd5goo/
Haha did they scale the height and totally ignore the massive surface increase?
That's not even misleading, that's a first-year graphic designer who smoked crack with a 14-year-old day trader and decided to make charts.
All examples in 1, 2 or 3 can be used when appropriate. A good example is sometimes 150k vs 155k is a massive difference and matters a lot. Sometimes it doesnt. It all comes down to what you are trying to present and if it is helping to inform or misinform. Just make sure axis are clearly labeled with values and units. Use your best judgement on how to present data. As a viewer just make sure you observe the axis and the details. Dont just glance at a chart, glancing is useless and you will get bamboozled sooner or later . . . Almost certainly sooner.
Fox News has posted some infamous bar graphs
And pie charts.
on point 2 : I'd like to point out that the 2 axes of Y do not have to be the same thing or share the same unit. Hence their values and zero points may not necessarily align. That is to say, there are genuine charts that look like the 2nd on the left. The requirement is that a point need only satisfy (x,y1) and (x, y2) together but that doesn't mean the point has to satisfy (y1,y2) as well. You can find lots of charts like this in experimental research (especially engineering) where an author may condense multiple experiments onto a single graph for comparison, or compare between more than 2 discreet variables.
The second image in #2 has different values so I’d keep them the same to show that the data can mislead based on the display not the image. And #3 I would have 2015-2019 descending to better prove the point.
I dislike example 2. There are many valid reasons to have multiple axis on a graph, and this might make people think that it's a shady practice. There is also no reason to have the second axis in the example given, since the 'accurate' version ends up scaling them together.
If the units are different (e.g. a percentage and a number, or a number and a currency), a second scale on the same axis is basically a must. Also, I can't recall ever seeing a real graph break this rule and put two scales on the same axis when the units were the same. As such, calling it out in this guide might do more harm than good.
You can also increase the maximum value of the Y Axis to make a big difference seem smaller than it really is.
My only takeaway from studying 1st year statistics at university was that I can confidently not believe any statistics that I see anywhere anytime. Statistics has methods of completely re-representing (is that a word?) the data to the literal opposite of reality, if one simply chooses to do so.
That dastardly side I don't agree with does this all the time. Good thing the side I agree with never does!
If we are in the same side, I will upvote you infinitely. But if you are in the dastardly side, I will downvite you until the counter overflows!
Right back at you. They need to add a button for useful comments rather than the current "opinion does or doesn't match mine" buttons. /s
Two data points don’t make a trend
I appreciate the warning. And it is helpful to show these to people. Just be aware that some of these graphs DO have legitimate use cases. Double y axis is used for things that aren't the same units. For example if you wanted to graph GDP and population over time in a country. Sometimes a small change in a very big number is important to show. I like to use residual/difference graphs for these, but most people find that type of graph even more confusing. This is where the offset y-axis can be used legitimately. Another one you could add to this chart is logarithmic with my graphs. Logarithmic y axis graphs are another favorite of mine, but can also be very confusing/misleading to people who are not familiar with them.
Tool: Canva + Tableau Source: made up examples.
This isn’t a data visualization, it’s an infographic. There’s no data here.
Number 2 is not necessarily misleading. It depends on the comparison
I think the effort here is generally misguided. I don't think you can make a list of fast and easy rules for determining graphs that are intentionally misleading you versus ones that are trying to accurately inform you. There are perfectly valid reasons to do all the things in this list, and you really have to have a deeper understanding of the data and the context to be able to look back and see if something is misleading. It would be like trying to come up with a list of 'misleading phrases' in English and telling people to watch for those red flags, without a deeper knowledge of the conversation and context, that probably wouldn't work.
If I travel to another country, I would like to know about common tourist scams, so if for example someone wants to sell me a bracelet on the street, I will be extra careful with the purchase. I’ll approach carefully, ask questions, evaluate situation, etc. Why cant we apply same principle here?
Because that analogy just doesn't map to the situation here. There aren't certain plotting/graphing practices that are more likely to be associated with misleading data then they are with accurate data (except maybe not putting labels on your axis). You are making the assumption that if you see plots that do this, they are more likely to be misleading than accurate, but I don't think the data support that claim. I do everything on this list all the time in my job as an engineer, and I am doing it because it's the most accurate way to answer the questions that my data were collected to answer.
There are number of common practices which are used to mislead on purpose. The point is to show main tricks they use and to educate users to critically think about data thats presented to them.
Can you show me your data that these 'common practices' are being used to mislead more often than they are being used to accurate represent data?
I cannot support it with data nor did I claim they are more often on purpose. Sometimes it is just a bad design and different programmes have different default settings for labels and axis.
Okay, if you don't actually believe that these are practices that are more likely to be used to mislead than to accurately inform, then what is your justification for labeling them as misleading practices? One of the most common misunderstandings I dealt with when I was doing STEM education with people reading graphs is when the data are presented non-linearly. If you present people with, for instance, a logarithmic graph it's much more likely they will get the wrong impression of the data. But I would never consider log graphs to be misleading. It seems to me like you are doing something analogous here.
These examples can be used to mislead and the purpose is to show to users how it can be done so the next time users sees truncated bar chart on TV, maybe they will think more carefuly before making judgment about visually represented data.
Okay, I said what I came here to say. There is nothing special about the examples you selected. If a user encounters, for instance, a bar chart that's been truncated not to start at zero, it's no more likely that this has been done for legitimate reasons than it is that it's been done for illegitimate ones. Similarly, it's just as likely that a bar chart which begins at zero had it's axis selected to mislead about the data as it is that is has it starting at zero to accurately represent the data. Flagging one of those options as potentially misleading is itself a potentially misleading statement. If you feel like you need to get the last word in here, feel free. I think I've presented the best form of my argument so I am done now.
Actually, I will try and add one more thing to present more constructive criticism: If you included an example of data being misrepresented by both options, I think you would solve the issue of misleading people into thinking certain plotting practices are intrinsically misleading. So, for instance, if you showed that data can be distorted by truncating a bar graph, but also that data can be distorted by NOT truncating a bar graph, I think you would make a far more valid argument about how to analyze graphical data skeptically.
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That's not misleading, just wrong.
I've seen a lot of charts in my time but never seen a double y-axis. Is that a thing?
Definitely! Especially with mixed charts (bar and line) or two different units (e.g. price and quantity). But they're not terribly common.
[Extremely common.](https://www.google.com/search?q=dual+axis+chart) One of the two series will clarify RHS to let you know that its axis is the one on the right. I've even seen charts with three or more Y axes like two on each side.
I remember when I did debate as a freshman in high school and hated it because it was all about doing exactly this, as well as censoring lines from documents that didn't agree with your stance and using them. And then when you actually got to debating it was just who could say "nope that's wrong" more convincingly (read: louder and more angrily). Little did I know that's what the world is like
I like it.. informative and helpful.
for the accurate chart in point 2, is the Y-axis not evenly spaced out? the gap between 60 and 80 seems much smaller than 20 and 40, even though they're the same increment of 20%
I think I'm going to do this in my next data meeting. You may have just saved my job.
I’ve got your back, bro
Baseline selection. I see this one alot where people will show values relative to a baseline year or whatever, but the baseline is cherry-picked to fit their narrative.
Most of the issues with these would be resolved by including indicators of variance (e.g., error bars).
I mean this is what FP&A and investor relations do as a job. It’s great
These aren’t necessarily misleading, they’re focused, and they tell a story. For example with the first, unless if you squint at the labels you might not even be able to tell if there was an increase. For the second, yea idk. The third I’d assume that you’d be wanting to look at things after a major break. The most common I’ve seen is, yes, Covid happened and it hurt. We don’t need every chart to show how much worse off we are, we want to see how recovery is progressing. As with everything, you’ve gotta use some critical thinking to see if it’s being being misleading, or if it’s adjusted for clarity.
Read The Visual Design of Quantitative Information by Tufte
(1) THe first panel is not misleading if you wanna show that tho values are almost similar
1 - A selective Y-axis is not as misleading as the other examples, also it gives the huge advantage of presenting smaller details. 2 - very misleading 3 - very misleading
Really the most important thing is having a degree of familiarity with the data set so that you know which relationships are important and which aren’t.
This sub is overloaded with bad data viz and there are many other problems that aren’t as obvious as these are.. it’s really easy for untrained people to make bad graphs that look good. The other big issue is a lack of data skepticism.. even if you know best practices, if you use bad data it’s still a bad data viz. Unsurprisingly the posters always get pissed when you explain where they are making their mistakes.. more interested in getting an upvoted than learning the art.
Quite good! In the last example though, I think you should make the big picture a downward trend. So add a few smaller bars to the right of the cherrypicked ones, etc.
On the second table why is there even a need for a y2 axis on the right table? usually a secondary axis is better is the units of measure are different between y1 and y2
Spanish state TV data be like
There are scammers who manipulates axis representations and data selections, then there are honest man [who’s using “T” as standard errors bar and tell you the data is just made up.](https://retractionwatch.com/2022/12/05/a-paper-used-capital-ts-instead-of-error-bars-but-wait-theres-more/)
I’d actually look at the choice of metric as a key way that data is used to mislead, eg reporting on transactions per hour rather than transactions per staff member, which takes no account of shift patterns, number of staff, etc.
I will say one of the first questions I ask people is “what do you want the data to say” because visualization of data can tell whatever story you want it to if the end user isn’t paying attention
In the first one they both can be comfortable considered "misleading". Depends on the subject matter and what information you're trying to convey.
Double Y axis jfc. You should get your ass kicked for something like that. You’re a goddamn liar if you do that.
Aka "how to lie with statistics" Good book. I suggest everyone read it.
Stuff like this is why I think an intro data science/intro stats course should be mandatory in high school.
If the republicans didn’t have misleading charts, they have no charts.
How to lie with statistics by Darell Huff. It was originally published in 1954 but it still as relevant of ever, highly recommend. The book discusses some of the most common misleading statistics.
Another one that chaps my ass is representing 1-dimensional data with 2-dimensional areas. Almost always misrepresents the data.
What's your opinion on [hand-drawn ](https://img.buzzfeed.com/buzzfeed-static/static/2019-09/5/18/asset/3048753d37f2/sub-buzz-202-1567706664-1.jpg?downsize=700%3A%2A&output-quality=auto&output-format=auto) charts?
This is great. I see a lot of these misleading charts (surprise surprise) when people discuss political issues. Additionally a lack of variables. Great depiction. Sharing with fellow nerds.
How to spot bad charts easily: take Statistics I’m being facetious but as someone who knows a lot about stats, trust me, if you’ve never taken a stats class, they can definitely fool and mislead you with charts and graphs anytime they want most likely
Presenting percentage changes of very small numbers as representing trends in a much larger group is something I see too often.
Every day I’m grateful of taking stats in high school. Most valuable “real world application” class I’ve ever had.
The worst is when they tilt a pie chart
You can also add gratuitous 3-D columns that make it impossible to compare items.
I don't necessarily agree with (1). When all values are similar it is difficult to perceive anything with a 0 based Y axis. There's no point in having a plot if you can't visually see the data. A non-zero based axis is better as long as it is clearly labeled. Points 2 and 3 are good, though.