Availability Bias

Have you ever said something like, “I know that [insert a generic statement here] because [insert one single example].” For example, someone might say, “You can’t get fat from drinking beer, because Bob drinks a lot of it, and he’s thin.” If you have, then you’ve suffered from availability bias. You are trying to make sense of the world with limited data.

People naturally tend to base decisions on information that is already available to us or things we hear about often without looking at alternatives that might be useful. As a result, we limit ourselves to a very specific subset of information.

This happens often in the data science world. Data scientists tend to get and work on data that’s easier to obtain rather than looking for data that is harder to gather but might be more useful. We make do with models that we understand and that are available to us in a neat package rather than something more suitable for the problem at hand but much more difficult to come by.

A way to overcome availability bias in data science is to broaden our horizons. Commit to lifelong learning. Read. A lot. About everything. Then read some more. Meet new people. Discuss your work with other data scientists at work or in online forums. Be more open to suggestions about changes that you may have to take in your approach. By opening yourself up to new information and ideas, you can make sure that you’re less likely to work with incomplete information.

Rahul Agarwal writing in Built in