We are all trying to make sense of how we got here and how we are going to get out — often with the aid of numbers and charts. The most common coronavirus numbers and charts fall into two categories: 1) those with actual data, which is often limited, and 2) those that predict what might happen based on assumptions. Many of the numbers and charts I’ve come across have reminded me of the dangers of Low-CAL data which is any data presentation that is low on clearly articulated: Context, Assumptions, and Limitations. Let’s talk about each one:
Context: Listening to President Trump’s daily briefings, I am reminded of the appeal of BANs (big ass numbers). Trump uses them a lot. For example, on March 23rd, he said that FEMA is distributing 8 million N95 respirator masks and 13.3 million surgical masks across the country. Sounds like a lot, but is it? Numbers, by themselves, have no inherent meaning. You have to put them in context. How does the supply compare to the need?
Assumptions: We make predictions about the future using assumptions which are based on currently available data and data about similar situations in the past or in other places. It’s the best we can do. That’s fine. But the assumptions and the rationale for the assumptions need to be clearly stated. For example, some predictions assume that the spread of the virus will slow down during the summer, but at the time of writing, we do not know how safe that assumption is.
Limitations: The data we have about the coronavirus is limited at the time of writing. We do not fully understand what factors promote or impede the spread of the virus nor do we fully understand how widespread it is. Many sick people have not been tested making it impossible to calculate a reliable death rate (number of deaths caused by the virus divided by the total number of cases.) Most data sets have limitations. To fully appreciate the implications of data, we must know what those limitations are.
Scholarly journals require that authors clarify context, assumptions, and limitations. But websites, tweets, blog posts, newspaper and magazine articles do not. I urge you to be a smart consumer on the lookout for Low-CAL data presentations. And when presenting data yourself, consider adding something akin to the drug facts label you find on medications to your charts, graphs, and maps. Somewhere in or near your data presentation include information on context, assumptions, and limitations so that viewers fully understand what the presentation does and does not show.
To see past data tips, including those about other chart types, click HERE.
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