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.

Related Posts

Principal Component Analysis With Faces

Mic at The Beginner Programmer shows us how to creepy PCA diagrams with human faces: PCA looks for a new the reference system to describe your data. This new reference system is designed in such a way to maximize the variance of the data across the new axis. The first principal component accounts for as […]

Read More

Using Uncertainty For Model Interpretation

Yoel Zeldes and Inbar Naor explain how uncertainty can help you understand your models better: One prominent example is that of high risk applications. Let’s say you’re building a model that helps doctors decide on the preferred treatment for patients. In this case we should not only care about the accuracy of the model, but […]

Read More

Categories

May 2018
MTWTFSS
« Apr Jun »
 123456
78910111213
14151617181920
21222324252627
28293031