In the field of statistics, there has been a lot written about statistical fallacies, logical fallacies, and fallacious reasoning. The following big list of fallacies is one that I like to use in my own undergraduate data science courses, particularly in my Data Ethics class where I teach my students about “lying with statistics”:
Many of these fallacies are relevant to data science modeling, including this one: Circular Reasoning, where the reasoner “begins with what he or she is trying to end up with; sometimes called assuming the conclusion.”
A broken clock is truly an example of circular reasoning (as the dial is circular, and the clock represents a particular measurement in a repeating circular perspective): “Even a broken clock is right twice a day.”
In the following article, I use the broken clock analogy for circular reasoning in describing the importance of verification and validation in predictive analytics models: “Are your predictive models like broken clocks? Here’s how to fix them.” The article also discusses the importance of training vs. test data sets, the bias-variance tradeoff in data science modeling, underfitting vs. overfitting, and the Goldilocks Principle applied to data science.
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