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Scientific Debt

David Robinson gives us the data science analog to technical debt:

In my new job as Chief Data Scientist at DataCamp, I’ve been thinking about the role of data science within a business, and discussing this with other professionals in the field. On a panel earlier this year, I realized that data scientists have a rough equivalent to this concept: “scientific debt.”

Scientific debt is when a team takes shortcuts in data analysis, experimental practices, and monitoring that could have long-term negative consequences. When you hear a statement like:

  • “We don’t have enough time to run a randomized test, let’s launch it”
  • “To a first approximation this effect is probably linear”
  • “This could be a confounding factor, but we’ll look into that later”
  • “It’s directionally accurate at least”

you’re hearing a little scientific debt being “borrowed”.

Read the whole thing.  I strongly agree with the premise.