Transparency as Simplicity

December 16, 2011

So the difference is: if the study is simple, you know what it means. If the study is complicated, you don't know anything. You have to trust the person that wrote the study.

That's not true for a regression study. Often, for many readers (myself included), the explanation is impenetrable. The references for the methods refer you to textbooks that are hard to find and technical, and, usually, there's no discussion of what the results mean, other than just a surface reading of the coefficients. If you want to truly understand what's going on, you have to read the study, and read critically. Then you have to read it again, filling in the missing pieces. Then you have to look at the tables, and back to the text, then to the model. And then you still probably have to read it again. And all this is assuming you already know something about how regression works. If you don't, you'll just have no idea.

Using an example from baseball, he advocates for transparency in research---not transparency in the sense of publicly sharing data and code, but in the sense of making research so simple that the reader can see straight to the data. No black boxes.With a tip of the hat to Holden Karnofsky.

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