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Bayes: Bayes' Theorem

Bayes’ theorem is probably the single most important thing any rational person can learn. So many of our debates and disagreements we shout about are because we don't understand Bayes theorem, or how human rationality often works. Bayes theorem is named after the 18th century Thomas Bayes, and essentially, it's a formula which asks: When you are presented with all of the evidence for something, how much should you believe it? Bayes’ theorem teaches us that our beliefs are not fixed; they are probabilities. Our beliefs change as we weigh up new evidence against our assumptions or our ‘priors.’ In other words, we all carry with us certain ideas about how the world works and new evidence will challenge us. For example, somebody might believe that ‘smoking is safe,’ that ‘stress causes mouth ulcers,’ or that ‘human activity is unrelated to climate change.’ These are their priors, their starting points. They can be formed by our culture, our biases, or even incomplete information. Now imagine a new study comes along which challenges one of your priors. Well, a single study might not carry enough weight to overturn your existing beliefs, but the studies accumulate and eventually the scales will tip. At some point, your prior will become less and less plausible. Bayes’ theorem argues that being rational is not about black and white. It's not even about true or false. It's about what's most reasonable based on the best evidence. But for this to work, we need to be presented with as much good quality data as possible. Without evidence, without belief forming data, we have only our priors and biases. And those aren't all that rational.