2021-01-29

diagnosing data-analysis issues

I had a useful meeting with Lily Zhao (Yale), Megan Bedell (Flatiron), and Matt Daunt (NYU) to discuss Zhao and Daunt's various data-analysis projects in precision spectroscopy. In both cases, we spent a lot of time looking at figures (and, in Zhao's case, interactively making figures in the meeting). This is generic: We spend way more time looking at visualization of issues than we do reading the code that generates them. I think it's important too; code has to make sensible figures; reading code can lead to all sorts of confusions. And, besides, debugging follows the scientific method: You hypothesize things the code could be doing wrong, you design figures to make that would demonstrate the bug, you predict what those figures should and shouldn't show, you make the figures, and you conclude and create new hypotheses. It's funny, I currently don't think that Science (tm) follows the scientific method, but I think debugging scientific code does. Hmmm.

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