Bayes' theorem is probably the most important thing any rational person can learn.

Many of our debates and disagreements about what we shout about are because we don't understand Bayes' theorem or how human rationality often works.
Bayes' theorem is named after Thomas Bayes, an 18th-century mathematician, and it is essentially a formula that asks: when you are presented with all the evidence about 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 new evidence against our assumptions or prior premises. In other words, we all have certain ideas about how the world works, and new evidence can challenge them.
For example, someone might believe that smoking is safe, that stress causes mouth ulcers, or that human activity is not related to climate change. These are their prior premises, their starting points. They can be shaped by our culture, biases, or even incomplete information.
Now imagine a new study emerges that questions one of your prior hypotheses. A single study might not be strong enough to refute your existing beliefs. But as more studies accumulate, the balance can tip. At some point, your prior hypothesis will become increasingly less plausible.
Bayes' theorem argues that being rational is not about black or white. It’s not even about true or false. It’s about what makes the most sense given the best available evidence. But for this to work, we need as much high-quality data as possible. Without evidence — without data to form beliefs — we are left only with our assumptions and biases. And these are not very rational.