Chapter 2 - Ideation and Research

The Difference Between Good and Bad Data

The fastest way to kill an infographic (especially a data visualization) is to include bad data. So make sure that you are always using good, useful, factual data in your infographics.

Good Data Is Properly Cited Back to the Original Source

Always cite the original source when using hard data. You should not find data on a webpage or in other infographics and consider that the source of the data. Always go back to and cite the original source of the information.

For example, a search to find “stats on drinking coffee” may return an article on Harvard’s website. But the webpage shows the source of the information as “source: National Coffee Drinking Trends 2010, National Coffee Association,” so search for that exact study to find the original source.

All sources for infographic content should be written at the bottom of the infographic in URL form.

Ig. Sources

Bad Data Is Misinterpreted

Data is misinterpreted when the writer inaccurately conveys what the data means. When data is represented from the wrong angle or stretched to tell the wrong story — it is called logical fallacy or coincidental correlation.

When dealing with stats, keep in mind that correlation is not causation.

Logical fallacy is when people wrongly assume: A occurred, then B occurred. So, A caused B.

Example #1
The area has a dense population of trees (A). The area has a high level of oxygen (B).
Misinterpreted Data: Trees grow better in areas with high levels oxygen.

Example #2
More young people are attending college (A). More young people are having juvenile delinquency issues (B).
Misinterpreted Data: Encouraging youth to go to college is corrupting them.

To prevent this, don’t search for data to prove a point. Instead use the data available to you to draw conclusions and create perspectives. This will prevent you from unintentionally manipulating data to prove a statement.

Good Data Is Connected to a Meaningful Message

People don’t remember stats, they remember relationships.

So only use stats when you find an interesting way to make the stat relatable. Only include stats that mean something and tell a story. Don’t include a flat stat that has no impact or message.

Bad Data Is Old

Good data is timely and current, so always check the post date of source pages and posts. Try to include information that is no more than 3-4 months old, and never use data that is more than a year old.

This will lower the chances of new data being released in the time between the posting of the stats and the publishing of the infographic. To help filter out old data, use Advanced Google Search to find recent data.

Good Data Is Double Checked for Quality Assurance

A good writer or researcher doesn’t rely on one source for all of their information. Avoid using one source for an entire infographic without verifying the facts with another resource (unless the original source is extremely reliable and trustworthy).

Verifying and double checking that facts are correct in the ideation and writing phases will prevent the headaches that happen when false facts are discovered during the design phase. Keep in mind that fact-checking and thorough researching is just as important as writing the infographic content.

Bad Data Is Made Up

Never, ever make up data for an infographic, and always confirm that any data you find is from a reputable source.

Confirm that the data you find is true by fact checking. Search for the same fact to confirm that you have the very best data available. Adding inaccurate data will kill an infographic, even if it is beautifully designed and meaningful. One false fact can undo all of the great work around it.

Factual, true data is extremely vital to the overall success of an infographic (even more important than design and stories), so never overlook the difference between good and bad data.

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