Oh, and the small numbers that they used not only on the axis but also as labels almost confirm they wanted to mislead their audience. Here is how it should have looked as you can see, the difference is not that impressive :. Writers also get themselves into trouble when they try too hard to be unique or creative with a graph. One of the first things they remove is the baselines or axis. I believe this is because the writers think it actually distracts from the data.
But it only makes the data harder to read! As you can see in this graph, there are no baselines at all! Now, this misleading graph makes it seem like our current president has nominated judges at double or triple the rate of his predecessors. When the graph is adjusted and the axis are added you can see the real story. It is important to remember that axis manipulation like this is not limited to just politics or hot-button issues. Even music journalists sometimes find themselves on the wrong side of a misleading or bad graph.
Just check out this bad graph that I found from Genius :. You can also see these kinds of misleading graphs it in tech:.
Honestly, wherever people are using data visualizations to backup their claims, you will probably find someone misleading graphs like the examples above. Related : What Is Data Visualization? Definition, Examples, Best Practices. A common trick of the graph manipulator is to blow out the scale of a graph to minimize or maximize a change.
This is known simply as axis changing in the data visualization world. Axis manipulation is almost the opposite of truncating data, because they include the axis and baselines but change them so much that they lose meaning. This is a very powerful tool on social media and can be used to push a false narrative. For example, take a look at this graph of global warming data from the National Review :. They are intentionally including temperatures from degrees up to degrees to make that line as flat as they can.
All to push an idea that global warming is not real or something. Thankfully, those who are more respectable with data called them out on their misinformation immediately. And the good people at Quartz decided to fix it for them, which you can see below:. The saddest thing is that these organizations know exactly what they are doing.
What should frustrate you, the reader, is that both of these organizations had access to the same data and tools. But only one decided to present it in a trustworthy manner. I mean, I could do the exact same thing using data about my beloved Arkansas Razorbacks. In this example, I looked at their win totals over the last 15 or so years and put them on a normal line graph. But I then took the same approach as the previous graph and manipulated the y-axis:.
With those changes, I can make it looks like the Razorbacks have been winning fairly consistently over the past few years. But if you follow college football you will know that we have not been winners most of the time, and the graph should look a little closer to this:.
Again, this is the exact same data, presented in the same type of data visualization, but each graph tells a completely different story. It only took one tiny change to completely flip the story. That should make you uncomfortable. This graph makes it seem like you have a terrible credit rating, causing you to freak out:. Even when you use , the max credit score , the graph they used is still very misleading:. And even though this faux pas seems rather simple in the grand scheme of things, this approach can be used to mislead people about their situation.
I wanted to include this example to show that not all misleading graphs are line and bar charts. Some can be innocent pie charts that just got caught up with some shady people! As you can see, this misleading tactic is being used in politics, on social media, and in businesses to push an agenda or idea. Another way to skew data is by only including certain parts of the data in your misleading charts or graphs.
For example, only including a month where there was a sales spike and not the rest of the yearly data. Or only sharing misleading poll results that make a certain person look favorable to everyone. But when you take a harder look at this graph, it only includes people from his own party:. And that party is shrinking by the day. This is definitely misleading, especially when the rest of the results looked like this:.
If someone were to quickly look at the original bad graph, they would probably think everyone loves the president. It is not technically wrong but it is definitely misleading.
This is often called improper extraction when only a certain chunk of data is included. This is more common in graphs that have time as one of their axes. I mean it is pretty easy to start with a year that confirms what you are trying to say. You could also call this tactic omitting data. When—you guessed it—some of the key data is just left off the graph.
In this case, I struggled to find some real-world examples, because who is really going to admit they left out data. But I did find this great example from Tejvan Pettinger on how someone could cherry pick some data to make a compelling but misleading statement.
In the first graph below, a reader could obviously be mislead into thinking that the UK National debt has never been higher! This graph could be used to justify a politician voting on some piece of legislation that would lower the debt. However, when you take a look at the full time series, you can see that national debt is actually pretty low in comparison.
This fictitious creator decided to also start the graph right at a low point and falsely illustrate that it could have been rising from zero to these rates. They also stuffed the graph with a bunch of random points to make it seem like the data set was much larger, when it only covered 10 years! If you want another example of improper extraction, look no further than the stock market. There are thousands of data points that stock analysts look at before they make trades or recommend people buy something.
So there are a lot of things that they can omit to make certain company stock look better or worse overall. But I think something that is very easy to mislead readers with is stock price.
For example, take a look at the graph below, which shows that Twitter has been on an large upward swing. As an outsider looking in, with just this graph at my disposal, I would think that they have been doing something right lately! They have been on an unprecedented slide for the past year or so, and that increase is just a tiny blip on the long term graph.
Now, if I were a less than honest stock trader, I could try to unload a ton of Twitter stock just by using that graph. And this type of misinformation could be used to manipulate about any piece of data you want to fit your goals. Like this example, which tried to justify climate change not being real:. Mostly because people do not want to take a look at the raw data and they see graphs as a beacon of honesty. I mean, why would someone lie on the internet, right?
So far, I have talked about intentional misinformation tactics that writers use to push their agendas. Now I think we should take a look at types of misinformation that can happen through sheer incompetence. This usually involves picking a type of graph or chart that does not fit the data you are trying to present.
And more often than not, the misunderstood pie chart is to blame for this. For example, take a look at this pie chart from the NFL Draft:. I am not sure what they were trying to do with this chart but as a multibillion-dollar company, they should have a competent graphics person. First, in what world is 64 prospects half of 69 prospects?
And second, why did they not use a bar graph for this data? If you were scrolling through your Twitter feed and saw misleading graphs like this, it would make sense that you thought USC blew the others out of the water. But if they wanted to share a more accurate graph, they should have created a column chart like this:. It may not be as flashy as the first one but at least it is accurate. Here is another questionable graph from the world of college football. This time, they attempted to graph projected win totals:.
They ranked each team correctly from highest to lowest, but the inclusion of the bar graph made no sense to most people. If your school had a longer name it looked like they would win more in this graph. And if you were quickly scanning a social media feed, that would be a fair conclusion. When it really should have been a timeline or even a simple table:.
I mean, what are they even trying to show with that terrible graph? In this example from Microsoft, by trying to be conceptual, they created a misleading data visualization:.
Even if Microsoft Edge is faster than Chrome or Firefox, it is just by a slight margin. Or if they still wanted to use something a little less boring, they could have gone with a bubble chart like this:. 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:. By examining real graphs we look at how the design can effect how we understand the data.
At the end of this page is a list of common ways graphs can be manipulated to mislead us and shape our opinions. But before looking at the list lets examine one misleading graph in detail. When looking at this graph, ask following questions: Is the scale is distorted? Was there any reason that the graph did not start a zero? Did you notice that the last entry 8. But this graph is also misleading.
Look at the y-axis. The problem is that humans are pretty bad at reading angles. In the adjacent pie chart, try to figure out which group is the biggest one and try to order them by value. You will probably struggle to do so and this is why pie charts must be avoided.
Once more, try to understand which group has the highest value in these 3 graphics. Also, try to figure out what is the evolution of the value among groups.
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