So we’ll call that break a “summer hiatus”. But now we’re back, and coming recently from the Joint Statistical Meetings (2019) in Denver, I’ve got Thoughts. This year’s JSM was different for me, because I spent most of my time on recruitment, speaking with potential applicants during many of the sessions. As a result, I attended many fewer talks that I normally do. By happenstance, the topic of the p-value came up repeatedly in the talks I was able to attend.
With the recent success of the Rockets, people are trotting out that old saw about analytics nerds ruining sports. With the Houston Rockets specifically, the question is a combined referendum on the numbers-based approach of GM Daryl Morey and the foul-drawing proclivities of Houston’s two stars, James Harden and Chris Paul. Of course, the latter is linked with the former, since analytics shows us that drawing shooting fouls is extremely efficient offense.
Disclaimer: This post is at least tongue-half-way-in-cheek. I acutally like the article I’m lampooning. A recent publication by academics and AI researchers titled “Data Sheets for Datasets” calls for the Machine Learning community to ensure that all of their datasets are accompanied by a “datasheet.” These datasheets would contain information the dataset’s “motivation, composition, collection process, recommended uses, and so on.” The authors, Gebru, et al., would you like to include more data about your dataset.
Not the bit about plays, the bit about sonnets. Writing song parodies is like writing sonnets Sonnets are an interesting because of how restrictive they are. According to American poet Aaron Kramer First, you are handcuffed by having to write fourteen lines. Then, you are shackled by having to write with a set meter. They put you into a sack called rhyme. But think what a magic act it is if you can set your meaning free!