The interaction between statistical methods (algorithms and data analysis) and actual work is critical and fairly obviously important. But articles like this show how it is still something we need to focus on.
My father, William Hunter, and George Box worked on these ideas and applied statistics decades ago.
Good article. Shows the risks/difficulty in applying machine learning models in the wild. Also shows the more sober side of applying ML models, where a domain expert says "Yeah, I know, I didn't need an ML model to tell me that". ML models will make mistakes based on bias, but hopefully in the long run they will outperform humans.
I agree with this approach for software devs in any business. One of the cool things one of my employers (a manufacturing company) did was send me out for a day to a plant and have me see how everything worked. It made some of the websites I was building for our plant workers much easier when I already had a good idea of what they needed it for.
My father, William Hunter, and George Box worked on these ideas and applied statistics decades ago.
Related: Two resources, largely untapped in American organizations, are potential information and employee creativity. https://williamghunter.net/articles/managing_our_way_to_econ...
The Scientific Context of Quality Improvement
https://williamghunter.net/george-box-articles/the-scientifi...