CURMUDGUCATION: The Trouble With Data

CURMUDGUCATION: The Trouble With Data

The Trouble With Data

Yesterday the Atlantic published an exceptionally helpful piece in the Science section by Robinson Meyer and Alexis C. Madrigal that offers some excellent explanation of why the nation has dropped the data ball for this pandemic. It's a good read from that perspective. But for education folks, there's more.

In the body of the article, Meyer and Madrigal share some observations about data, and the problems with data-driven anything; these points are important, and should be emblazoned on the office door of every data-driven follow-the-science policy maker and administrator in the country.

1. All data are created; data never simply exist.

Before March 2020, the country had no shortage of pandemic-preparation plans. Many stressed the importance of data-driven decision making. Yet these plans largely assumed that detailed and reliable data would simply … exist. They were less concerned with how those data would actually be made.

Here come the data

Data have to come from somewhere. They have to be created, and then they have to be interpreted. Anyone who assumes that the data are good simply because they exist--well, that's a terrible assumption. Every step pf the data-creation chain, from the testing instrument, to scoring, to score conversion, to interpretation of the score--all of that should be questioned and examined and then questioned again.

But in our high stakes testing era, that has not happened (nor is it happening now). When the state says, "22% of your students are below basic in reading non-fiction," that's not a figure that descended from heaven in a burning memo. It's a number that was created, and everyone ought to be asking how it was created. Starting with a CONTINUE READING: CURMUDGUCATION: The Trouble With Data