These days we are all learning more salient information at a constant clip. Information about the spread of viruses, systematic racism, climate change, how a pandemic exacerbates inequality. If you aren’t learning, you aren’t paying attention.

But learning isn’t quite enough, we have to update our priors. This is Bayesian statistics shorthand for modifying your prior beliefs and knowledge based on new data. It’s okay to change your viewpoint, in fact, it’s necessary.

Bayes was an 18th century Presbyterian minister who also figured out a lot of math. Including an incredibly simple but powerful algebraic formula for the probability of events based on the probability of other events. But the key here is weighting those events/observations. As you learn more, you decide how much weight to give the new knowledge. You don’t delete or replace the old data, you just weight it differently.

Recently, Bayesian statistics got a headline in the New York Times for its use in epidemiology. (And the article includes a quote by my Harvard professor, Joe Blitzstein.) Among tech folk and data scientists, it’s also popular lately because of AI - including the type in driverless cars.

However, it turns out to just be good framework for a logical mind. Learn more, update your beliefs, then go learn more, repeat.