Decision Errors in Data Science
From Big Data Analytics lecture 2, I was most impressed by the slide concerning decision errors in logic.
I imagine most data scientists are fans of Mr. Spock. No need to be in the Captain’s Chair, but a strong need to contribute meaningful analysis to important decisions.
Any Star Trek fan can quote Mr. Spock’s sage observation, “Logic is the beginning of wisdom, not the end.”
Logic is critical to data science, and the wisdom that can arise. However some logical errors can arise, as pointed out by Dr. Wang’s slide:
Typical Decision Errors: Logic
- Not asking the right questions
- Making incorrect assumptions and
failing to test them
- Using analytics to justify instead of learning the facts
- Interpret data incorrectly
- Failing to understand the alternatives
My Geographic Information Systems – Spatial Databases and Data Management course instructor (Dr. Ralston) has a graphic on his door about “correlation and causation.” His graphic shows a link between decreasing use of Windows Internet Explorer and a correlated decrease in murders.
The refrain is always “correlation does not imply causation.” Logic might be sound, the math might add up, but the pitfalls exist.
I often wonder if some of the data science “boot camps” and workshops can effectively impart these key lessons that are central to the process of science.