Why Is It Important to Learn About Bad Graphs and How to Avoid Misinterpretation

Learning about bad graphs is crucial because it helps you avoid misinterpreting data, which can lead to poor decision-making. Misinterpreting data can have serious consequences, such as investing in a failing business or supporting a policy that's not effective.

Bad graphs can be misleading, making it difficult to understand the actual trends and patterns in the data. A graph with a misleading scale, for example, can make a small change appear much larger than it is.

To avoid misinterpretation, it's essential to understand the basics of graph design, such as choosing the right scale and axis labels. This will help you create clear and accurate graphs that convey the information effectively.

By learning about bad graphs, you'll become a more discerning consumer of data and a more effective communicator of information.

We often take information at face value without questioning it, but this can lead to poor decision making if the information is misleading. Typical math classes don't teach how to identify manipulated graphs.

The media can easily mislead people with graphs that are designed to deceive. We're usually too busy or distracted to question the information fed to us. Falling for bad graphs can have serious consequences.

A good graph should have clear and accurate labels on its axes, but often these labels are misleading or omitted. Consider the study's data and whether it provides a good representation of the research objective.

Behind every graph is a person or organization with their own motivations and biases. These factors can influence the depiction of the graph and lead to misleading information.

Misleading graphs are a powerful tool for manipulation, and it's essential to learn about them to avoid falling victim to false narratives.

Omitting the baseline is a common tactic used to distort data. This involves leaving out the starting point, making it seem like a trend is more significant than it actually is.

Manipulating the Y-axis is another technique used to mislead. By adjusting the scale, a graph can make a small change look like a massive difference.

Cherry picking data is a straightforward approach to misleading graphs. It involves selecting only the data that supports a particular argument, while ignoring the rest.

Using the wrong graph is a subtle way to deceive. Choosing a graph type that doesn't accurately represent the data can lead to a false interpretation.

Going against conventions is another way to create confusion. Deviating from standard graph layouts and colors can make it difficult for viewers to understand the data.

Here are some specific examples of misleading graphs:

Presenting data in a misleading way can be done intentionally or unintentionally, and a graph can only be as accurate as its maker allows it to be.

One common mistake is omitting the baseline, which can make a graph appear more dramatic than it actually is. This can be seen in the example of the graph that made it seem like you have a terrible credit rating, causing you to freak out.

Another mistake is manipulating the Y-axis, which can make a graph appear to show a larger change than it actually does. This is known as axis changing in the data visualization world, and it can be used to minimize or maximize a change.

Using the wrong graph can also be misleading, as seen in the example of the pie chart that added up to more than 100%. This is a common problem when people try to visualize survey data that has multiple answers.

The y-axis has also been manipulated in some graphs, making it seem like a larger change is occurring than it actually is. This can be seen in the example of the graph that showed the number of cases of COVID-19, where the y-axis was manipulated to make it seem like the virus was spreading slowly.

A table of common graphing mistakes is shown below:

A common mistake is also to include way too much data in a single graph, which can confuse more people than help them. This can be seen in the example of the graph that showed the rise in cases of COVID-19, where there were too many trend lines and the legend didn't help decipher anything.

Finally, using the wrong type of visualization can also be misleading, as seen in the example of the graph that showed the different ages at which young people leave their parents' homes in European countries. The graph type used was confusing and didn't help the reader understand the data.

Comparing apples to oranges is a perfect example of how to drive misinformation with a misleading graph. The absolute number of serial killers per country is shown on a bar chart, but it's not in proportion to the country's inhabitants.

A country with 300M inhabitants, like the US, gets a huge horizontal bar, making it a poor example of data analytics. This kind of comparison is confusing and doesn't accurately represent the data.

A map below the chart makes no sense in relation to the chart data, leaving viewers unclear about what the dots represent. There's a lack of tooltips or info panels to provide context, making it even more complicated.

Misleading graphs can make things even more complicated and confusing, rather than simplifying the underlying data.

Learning about bad graphs is crucial because it helps you identify and avoid misleading or confusing data visualizations.

A classic example of a bad graph is the 3D pie chart, which is often used to show how different categories contribute to a whole.

Using 3D effects can make a pie chart look more interesting, but it can also create a false sense of depth and make it harder to read.

A bar chart with overlapping bars is another type of bad graph, as it can make it difficult to compare the values of different categories.

This type of graph is particularly problematic when the bars are not labeled clearly, making it even harder for the viewer to understand the data.

A graph with inconsistent or missing labels is another red flag, as it can make it difficult to understand what the data is showing.

A graph with too many colors or fonts can also be overwhelming and distracting, making it harder to focus on the data.

A graph with too much detail can also be a problem, as it can make it difficult to see the big picture and understand the overall trend.

Good graphs are essential to accurately convey information, and they're often a reflection of the creator's understanding of the data.

Misleading graphs, on the other hand, can be intentionally created to shift blame or unintentionally created due to a lack of knowledge in visualizing data correctly.

A good graph should be clear and easy to understand, like the ones created with Venngage mentioned in the article.

By learning about bad graphs, we can avoid falling into the trap of misinterpreting data and make more informed decisions.

Understanding good graphs requires attention to detail and a critical eye. A well-made graph is the result of meticulous research, presenting the data as it is, and fitting graphing decisions.

To create a good graph, it's essential to follow graphing conventions. The y-axis should be set to zero, and the graph should not be manipulated to mislead the audience. As we've seen, omitting the baseline, manipulating the y-axis, cherry picking data, using the wrong graph, and going against conventions are all common mistakes that can make a graph misleading.

A simple example of a poorly made graph is one that uses an insane color palette, making it difficult to read and understand. On the other hand, a graph that presents data in a clear and concise manner, such as a bar chart, can be much more effective.

Here are some tips for creating good graphs:

By following these tips, you can create graphs that are accurate, informative, and easy to understand.

Going against conventional color associations can be misleading. Red is often used to represent Democrats, while blue represents Republicans, and vice versa.

Using green for losses and red for profits is a poor choice, as it goes against almost every map data visualization. This can be a tool to manipulate an audience.

A single color palette with shades and tints is usually more effective than using a variety of colors. This is because almost everyone knows how to decipher those types of maps.

Flipping a graph upside down can be a blatant attempt to push a false idea. This was done to make it look like gun deaths were going down when in reality, they were spiking.

Including random time values on the x-axis can be a form of omitting data. This was done to fit a narrative.

A terrible scale can make a graph confusing. The scale used in one example had a big difference between 10,001 and 100,000 – especially in smaller countries.

Using a muted color for high values can be confusing. In one example, the red that denotes 100k+ or more cases is the darkest, but it's not the most eye-catching color.

Going Against Conventions is a misleading tactic used in data visualization to intentionally confuse or mislead the audience. This can be done by altering long-held conventions or associations, such as using red to represent Democrats and blue to represent Republicans.

Using green for losses and red for profits is a classic example of this tactic, making no sense to a competent graph maker but perfect for manipulating an audience. A competent graph maker would use a single color palette with shades and tints to make the map easy to decipher.

The article provides an example of a map where dark colors are used to denote high values and light colors are used to denote low values, but the opposite is true. This is a clear case of going against conventions, making it difficult for the audience to understand the data.

Flipping a graph upside down is another egregious example of this tactic, making it look like gun deaths were going down when in reality they were spiking after the Stand Your Ground law was enacted. This is a blatant attempt to push a false idea to the audience.

A graph that includes random time values on the x-axis is another example of omitting data to fit a narrative. This is a deliberate attempt to mislead the audience, and it's a bad sign if a brand thinks so little of your intelligence that they push bad graphs on you.

Using a ratio of cases to the population of each country instead of just a number would have painted a more accurate picture on a map. A terrible scale and confusing color selection on a map can make it difficult for the audience to understand the data.