A truncated graph has a y-axis that does not start at zero. These graphs can create the impression of important change where there is relatively little change. Truncated graphs are useful in illustrating small differences. Graphs may also be truncated to save space.
Commercial software such as MS Excel will tend to truncate graphs by default if the values are all within a narrow range. Both of these graphs display identical data; however, in the truncated bar graph on the left, the data appear to show significant differences, whereas in the regular bar graph on the right, these differences are hardly visible.
A perspective 3D pie chart is used to give the chart a 3D look. Often used for aesthetic reasons, the third dimension does not improve the reading of the data; on the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension.
The use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts. In a 3D pie chart, the slices that are closer to the reader appear to be larger than those in the back due to the angle at which they're presented. In the misleading pie chart, Item C appears to be at least as large as Item A, whereas in actuality, it is less than half as large. Graphs can also be misleading for a variety of other reasons. An axis change affects how the graph appears in terms of its growth and volatility.
A graph with no scale can be easily manipulated to make the difference between bars look larger or smaller than they actually are. Note that is missing from these charts; Politifact was unable to find that report, as were we. This has several problems.
But ultimately, this is a column chart, and column charts must always start the y-axis at zero. Not quite as dramatic there, is it? So did the White House pull these graduation rates from multiple datasets? Different data collections can count graduation rates in different ways, so that would be problematic.
In chart-making, choosing the appropriate form to represent the data on hand is of utmost importance. And when you want to show subtle changes in rates over time, you use a line chart.
There are a few things we can get out of this larger context. One is that graduation rates were already trending upward when Barack Obama became president. Another is that, as of , the percentage point increase in graduation rates was higher under Obama than any previous president. The above section has been updated. This is the chart we should all think about when someone says line charts should always have a y-axis starting at zero.
In the example below, The Intercept was trying to show how the Russia issues have taken over the news lately:. It fell just a little bit short, mainly because the labels they chose are not very descriptive. And unless you calculated it yourself, you were left guessing what the actual split was between the two. If I was creating this visualization, I would have gone straight to the pie chart:.
Not only does it include the same information, it makes it easy for someone to quickly spot the difference. To conclude our list of misleading data visualization tactics, I thought it would be a good idea to look at misleading graphs and charts that alter long-held conventions or associations. If you are a little confused with what I am talking about, think about a graph where red represents Democrats and blue represents Republicans.
Or a simpler example is using green for losses, and red for profits. That would make no sense to a competent graph maker but would be a great tool to manipulate an audience. In this map about STI rates across the country, they choose to use a dark color to denote low levels and a light color for high levels:. This use of color goes against almost every map data visualization I have even seen.
So I do think it was created to intentionally mislead the reader. And to make things even more mind-boggling, the higher the number is, the lower the rates are, supposedly.
So the map is confusing all around, which could make someone rightly think that Idaho is a hotbed for STIs, when it is really the southern states. The dark colors are used to denote high values and the light ones are low ones. Here is another example of a map using an insane color palette:. Could you even follow what they were trying to say? Just like I pointed out above, these map makers should have used a single color palette with shades and tints.
Almost everyone knows how to decipher those types of maps. Next, we have a graph where the writer wanted to outright push a false idea to its audience. Honestly, it is one of the most egregious graph manipulations I have ever seen because of how blatant their intent was. They actually flipped a graph upside down. This made it look like gun deaths were going down when in reality, gun deaths were spiking after the Stand Your Ground law was enacted:.
A simple rotation and mirroring of the graph will show you what it should have looked like:. And for our final example, we have one from our favorite data manipulators, Fox News. Did you spot it? And they are counting on that. If you take a look at the x-axis, you will see that they choose to include a bunch of random time values for their graph. You could also consider this an example of omitting data. And the worst part about this example is not that it is a bad chart, but that they thought they could dupe their loyal viewers.
If a brand thinks so little of your intelligence that they push bad graphs on you, I would recommend finding another source. One of the most common mistakes I have seen people make when creating graphs and visualization is that they include way too much data.
Trying to include all the data on one visual usually confuses more people than helps them. For example, this graph that was shared by al White House official a few weeks ago tells us almost nothing about the rise in cases. After you look at the first two trend lines, the rest of the graph is basically worthless, unless you have a magnifying glass handy. A better way to present this data might have been an interactive graph that you can isolate your state from the others, or just create single graphs for each state.
The main thing that sticks out to me in this map visualization is that they used absolute numbers as their data source. Instead, they should have compared the number of cases to the population of the country, city or state. This would have made comparing the different locations a lot easier. It also would have made the chart actually useful.
Like the last example this map literally tells us nothing useful, except where the biggest cities in the country are. Another example of using the wrong data can be seen in this pie chart from the Georgia Dept. In this pie chart, they put every adult under 60 years old in one group, and then anyone 60 years or older in another:. The other group makes up more than half of the pie chart!
Those over 60 are the most impacted by the pandemic. This is a common problem when people try to visualize survey data that has multiple answers. The creator of the original pie chart should have presented the data kinda like this:.
Statista presents very similar data in an effective way in this simple bar chart:. As you can see, both the x-axis and y-axis have been manipulated, which is very common on graphs like this. Instead of starting using a consistent scale on the y-axis of this graph, they jump from 0 to 5, and the to 20, On the x-axis the number of days between each important date are inconsistent as well.
In only a handful of days, the number of cases has basically tripled or more. Here is a rough estimate of what that graph should have looked like:. By using accurate axes in this revised example shows truly how fast the virus was spreading in the later days of this chart. The other graph is a lot less clear, which could lead some viewers to believe that we have this thing under control. For our final example, I found another visualization that attempted to map the number of cases throughout the world.
This one goes against normal color conventions that you would see on a similar map:. First, they really should have used a ratio of cases to the population of each country instead of just a number. That would have painted a more accurate picture. Especially in smaller countries across the world. But the worst part of this graph is the colors used to show that scale—I really have no idea what they were thinking when they picked that palette.
Then it just goes downhill from there with the color selection once you take a hard look at each. Why does the scale go from a subdued color to a very vivid color and then back to a subdued color? But if you have any ideas, please let me know in the comments! What blows my mind is that they could have just picked lighter shades and tones of a single that dark red to build an effective scale of colors! Would you have guessed there were that many brands that play loose and fast with graphs?
As with any type of news story, I would recommend first checking where the graph is coming from and then taking a look at the data.
Like I said in the intro, most people sharing misleading graphs do not have your best interests in mind. For example, if a graph that shows the benefits of coconut oil is being shared by a company that just happens to sell coconut oil, that graph may be skewed.
Maybe look at some other sources before you order a case of it. And if it is just one person or group sharing this particular graph, that is another red flag. So be vigilant by always checking your sources, stay skeptical and if you feel like a writer is being misleading, call them out on it! Do you want to learn more about picking the right charts for your data?
Read this guide guide:. If the graph was drawn correctly it would look like this:. This graph is not as shocking however and so is far less likely to be used in a news story.
Look at the following example:. This graph is completely meaningless without a label on the y-axis — it is impossible to interpret the graph without it. The way the graph is drawn could also fool you into thinking that the number of CD singles sold in was greater than in when actually they are the same!
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