The Importance of Data Literacy in a World of Fake News

The Importance of Data Literacy in a World of Fake News

As the proverb goes, there are three kinds of lies: lies, damned lies, and statistics.  In a world where data is readily available and statistics are easier to fabricate than ever before, understanding numbers and where they come from is increasingly important. We live in an age of “fake news” and “alternative facts” and recognizing which presented graphs and statistics are not fake is crucial. Looking at a chart and recognizing its skewness or significance, or recognizing any bias that it portrays may seem useless for “non-math” people, but it is essential for forming your own opinion, and not just swallowing information.  

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Let’s look at the above first example from 2012.  It looks like the top tax rate is nearly quadrupling.  However, looking closer, it is clear that the top tax rate only increases by about five percent.  A person who does not necessarily look closer into data that is presented to them may not check to see where the x-axis begins or to look at the numbers and decide that they aren’t as extreme as they look. The selected y-axis starts at 34 and not zero, which makes this chart incredibly misleading because the gap between 35 and 39.6 so large. It is extremely easy for people to create outright false and inconsistent graphs by changing axes, not specifying percent or quantitative increase, or simply making a difficult to read graph with multiple colors and fonts. Through these methods, the biased media can shift views based off of misguided charts and graphs.

Thinking critically about statistics is key to understanding current events. One statistic that may seem shocking and positive is that there haven’t been any mass school shootings since May.  It is easy to look at that statistic and think that the government has somehow put an end to school shootings, and we should not predict anymore in the near future. However, thinking about that slightly deeper, you realize that school has only been in session for about one month in most places in America.  This is a simple example of how a perfectly true statistic may not be as straightforward as it looks. Though this is a simple example, knowing when to ask questions and which ones to ask is a result of critical thinking that data literacy can teach you.

Another example is a high infant and child mortality rates decreasing life expectancy.  For example “From 1900 to 1998, life expectancy from birth for Americans rose from 47 to 75, an increase of 28 years”  (Why Life Expectancy is Misleading).  This seems incredible; it is an increase of 28 years that people are living from birth. However, from 1900 infant mortality rates have decreased about  14 percent. This makes it seem like Americans are much healthier overall. However, a large reason that this happens is that infants are no longer dying at incredibly high rates from the ages of zero to one.   Having fewer young deaths increases the average age that people live to, despite that number not increasing too much from 1900. For this reason, you cannot say that people are likely to live 28 years longer than they used to, but you can say that infants are much more likely to live to their teen years.  Being able to look at weighted averages and see where the data may be difficult to read is key.

Furthermore, being able to recognize bias is a critical part of being a citizen.  Data does not necessarily always present itself as a fact but as an opinion. Qualitative data is even easier to misunderstand than quantitative, and recognizing the source of it is essential in determining whether or not it can be trusted at face value.  Asking school children if learning how to read is essential towards them being productive citizens will not return results that are necessarily useful. They may not want to learn how to read and therefore say that reading is useless. In the same way that asking people their opinions of a candidate at a rally will yield different results than asking the same questions at a protest against one of the candidate's policies. Being able to recognize bias within questioning and towards people is an essential part of data literacy that should be more integrated in society.  Maybe, if more people recognize that polls aimed towards CNN and FOX watchers will yield different results, people will not necessarily trust them as much.


Works Cited

“Why Life Expectancy Is Misleading.” Priceonomics, 11 Dec. 2013, priceonomics.com/why-life-expectancy-is-misleading/.

Fletcher, Richard, et al. “The Impact of Greater News Literacy.” Digital News Report, www.digitalnewsreport.org/survey/2018/the-impact-of-greater-news-literacy/.

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