Four short links: 22 June 2015

Power Analysis, Data at Scale, Open Source Fail, and Closing the Virtuous Loop

  1. Power Analysis of a Typical Psychology Experiment (Tom Stafford) — What this means is that if you don’t have a large effect, studies with between groups analysis and an n of less than 60 aren’t worth running. Even if you are studying a real phenomenon you aren’t using a statistical lens with enough sensitivity to be able to tell. You’ll get to the end and won’t know if the phenomenon you are looking for isn’t real or if you just got unlucky with who you tested.
  2. The Future of Data at ScaleData curation, on the other hand, is “the 800-pound gorilla in the corner,” says Stonebraker. “You can solve your volume problem with money. You can solve your velocity problem with money. Curation is just plain hard.” The traditional solution of extract, transform, and load (ETL) works for 10, 20, or 30 data sources, he says, but it doesn’t work for 500. To curate data at scale, you need automation and a human domain expert.
  3. Why Are We Still Explaining? (Stephen Walli) — Within 24 hours we received our first righteous patch. A simple 15-line change that provided a 10% boost in Just-in-Time compiler performance. And we politely thanked the contributor and explained we weren’t accepting changes yet. Another 24 hours and we received the first solid bug fix. It was golden. It included additional tests for the test suite to prove it was fixed. And we politely thanked the contributor and explained we weren’t accepting changes yet. And that was the last thing that was ever contributed.
  4. Blood Donors in Sweden Get a Text Message When Their Blood Helps Someone (Independent) — great idea to close the feedback loop. If you want to get more virtuous behaviour, make it a relationship and not a transaction. And if a warm feeling is all you have to offer in return, then offer it!
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