We live in a world where data has become more important than ever. In fact, the World Economic Forum calls data the new “It currency”, every bit as valuable as oil and gold. Yet, despite the fact that data has an enormous impact on our daily lives and the way we do business, many people don’t really know how to read data. They don’t always know how to interpret statistics or graphs, and they don’t recognize when data is used incorrectly or in a misleading way.

Literacy is defined as the ability to read and write; using the written or printed word to gain and share knowledge. Similarly, data literacy is the ability to read and communicate data as information and use it to draw conclusions and make decisions. Data literacy therefore involves two skills: an understanding of statistics as well as critical thinking.

### Seeing the Big Picture

*Brian Fantana: “They’ve done studies, you know. 60% of the time, it works every time.”
Ron Burgundy: “That doesn’t make sense.”*

(Anchorman: The Legend of Ron Burgundy, 2004)

Many people are generally averse to dealing with numbers, but keep in mind that you need statistics to make sense of data, and statistics is, at its core, a mathematical science. Hence, you have to get comfortable with basic math in order to become truly data literate. Here are a few other aspects to pay attention to.

#### Consider context

A single statistic doesn’t mean much on its own. Always consider the context:

– Is the latest figure different from previous ones?

– Does the statistic seem to vary over time?

– How long has data been collected for?

– Is the statistic comparing “apples to apples”?

– Can the statistic be measured in another way?

– What are the assumptions behind the statistic?

– Does the statistic tell the whole story?

Bear in mind that statistics focuses on reaching general conclusions from limited data. It can be very difficult to draw conclusions that would remain appropriate and true even if you significantly increased the size of the sample dataset or used the entire population. In other words, trends observed in a dataset of 100,000 customers may disappear when you look at 5,000,000 records or used data for every single customer on the planet.

#### Don’t jump to conclusions

The human brain excels at spotting patterns, which makes it easy to presume that perceived patterns in random data might be trends. That is precisely why we use statistical tests: to determine if, or when, such patterns are, in fact, trends. Consider how, why, and when a statistic was created, and whether the creator might have a hidden agenda. It’s easy to read too much into a statistic when certain facts were withheld for shock value or to sell a product. Finally, watch out for ambiguous conclusions like “could lead to”, “might be responsible for”, or “typical”.

#### Be clear about terminology

Some statistical terms do not mean what you think they do. For example, when a relationship between variables is statistically significant, it does not necessarily imply that the effect is huge or important. Instead, it means that it’s highly unlikely that the relationship is simply due to chance. Another concept that is often misunderstood is that correlation does not equal causation. Just because there’s an association of some sort between two variables, it doesn’t mean that one causes the other. There might be a third variable that affects both, or the correlation could simply be a coincidence.