Tag Archives: Human side of statistics

Recommended: How a Feel-Good AI Story Went Wrong in Flint

Building a great statistical model does no one any good if it doesn’t pay attention to non-statistical issues. This story talks about a machine learning model to identify which houses in Flint Michagan that were the best candidates for removal of lead pipes. The model worked fairly well, but came up against problems like individual city council members wanting to assure their constituents that enough was being done in their district. I’m not sure what the actual moral of this story is, but it does serve as a warning to be careful when you are modeling data in a contentous area. Continue reading

Recommended: How to be more effective in your professional life

This article starts with a nice anecdote about being dismissive about what someone else is saying ends up hurting you. It also provides a nice structure, POWER, for organizing consulting meetings. POWER stands for Prepare, Open, Work, End, and Reflect. This article was a basis for some of the content in an interesting webinar on consulting. Continue reading

PMean: What are we doing to justify all that time we’re budgeting?

An email discussion about the appropriate percentage effort on research grants has produced a lot of interesting discussions. One person raised an interesting question. The typical data analysis, he claimed, might involve a few hours reviewing the input data set, a few hours conducting the analysis and a few hours preparing a statistical summary, but even after a generous estimate of the work at each of the time points, he could only come up with 22 hours of effort, which corresponds roughly with a 1% FTE. I wrote back describing some of the things that might occur before the data analysis that might add time to this effort. Continue reading