Five Cognitive Biases In Data Science (And how to avoid them)

Five Cognitive Biases In Data Science (And how to avoid them)

Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.

Recently, I was reading Rolf Dobell’s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before. I realized how deeply seated some cognitive biases are. In fact, we often don’t even consciously realize when our thinking is being affected by one. For data scientists, these biases can really change the way we work with data and make our day-to-day decisions, and generally not for the better.

As data scientists, our job is to make sense of the facts. In carrying out this analysis, we have to make subjective decisions, though. So even though we work with hard facts and data, there’s a strong interpretive component to data science.

As a result, we data scientists need to be extremely careful, because all humans are very much susceptible to cognitive biases. We’re no exception. In fact, I have seen many instances where data scientists ended up making decisions based on pre-existing beliefs, limited data or just irrational preferences.

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