How often have we seen a graph in an opinion piece without knowing how it was created, but somewhere in the back of our mind we suspected that it was tweaked somehow to make a point? How can we ferret out “creative analytics” from the true story? Remember Mark Twain’s famous quote about ” … lies, damned lies and statistics.” It is much more difficult to identify “lies” when we cannot inspect the data behind them.
By necessity, we always make choices about how to present data. After all, we *are* trying to make a point when we share information. But even if we do not intent to spin the message, we may be unable to see the whole story until someone else adds their insight. By making our data available for download, we can level the debating field somewhat and hopefully reach better informed conclusions.
Whether by accident or by design, one way to spin the message involves the use of data ranges. In the example below, we have divided US obesity rates into three different ranges. The first range uses intervals of 11, the second range uses intervals of 10 and the last range uses intervals of 5.
Look at the graphs about soda taxes in vending machines and see how each graph may lead to a different conclusion about obesity and soda taxes in vending machines. Then take a look at the graphs for the other taxes and notice how those graphs support similar conclusions regardless of the range size.
When deciding how to present information we have to balance “information overload” with the need to present important details. Which graph we choose ultimately depends on the point we are trying to make. Some might call that spin, others call it effective communication. If we are the audience, we need to be skeptical and ask questions.
Related Posts:
http://pharma-bi.com/2010/03/how-to-avoid-misleading-conclusions-explore-your-data/
http://pharma-bi.com/2010/03/tableau-public-interactive-obesity-data-on-the-web/