Goofus Data

Goofus image

When I was a kid, one of the only exciting things about going to the dentist was the chance to catch up on my Highlights magazine reading.  The childrens’ magazine is famous for a monthly feature titled “Goofus and Gallant” which showed the behaviors of good children versus those of not-so-good kids.

I was reminded of these cartoons as I sat, frustrated once again, listening to the media and politicians discuss Covid data. If you wanted to put together some real life Goofus examples for dealing with data you don’t have to look any further than the local or network news. From “garbage in, garbage out” to mistaking the data as the end and not an input to a deeper insight Goofus seems to be hard at work daily.

Don’t have unclear/inconsistent reporting standards.

What is the definition of a Covid death? When do numbers get reported (even on Sunday?)

Don’t focus on the wrong data.  

Infection count is only useful or important in the context of audience size or tests conducted.

Don’t look at daily data if the system operates on a different time scale.

We know there is a lag between action and impact with Covid.  Would a rolling 14 day average be more useful for planning and trend analysis?

Don’t lose the message in averages.

Pull out a few early states, or remove the elephant that is New York and watch how the chart of the country’s battle changes.

Don’t use the wrong units.

Percentages can be a marketers friend (100% growth of a small number sounds better than the actual number) but sometimes it is also the best way to understand the data. Percentage (%) of beds in use versus number (#) of hospitalizations is more readily understandable when ICU beds are a key capacity constraint.

Those are just some of my daily irritants.  And don’t get me started on false positive % or how an exponential function works (just watch this old shampoo commercial.) https://youtu.be/mcskckuosxQ

Do you work with data?  What would you add?

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