How to Make Smarter Decisions with Bayesian Thinking

Excellent intro to Bayesian thinking: when struggling to function in a world of uncertainty, "Bayes offered an elegant solution. Instead of demanding absolute certainty... he provided a mathematical framework for learning from experience... to systematically update our beliefs as new evidence emerges, transforming probability from a static measurement into a dynamic tool for understanding".

Developed in the 18th century, it's incredibly relevant and useful today: "when social media amplifies certainty while burying nuance, when we’re degenerating into ... polarized, angry keyboard warriors, Bayesian thinking... teaches us that knowledge isn't about achieving perfect certainty, but about systematically reducing uncertainty", to think like a scientist: "our beliefs should be fluid... ready to adapt when new evidence emerges... reason systematically about what we know, remain humble about what we don't, and update our understanding as new evidence emerges".

At its heart, "Bayes’ Theorem tells you how to update your understanding when faced with new evidence."

Wetenberg then provides and explains the mathematical formula, which relates:

• "Posterior probability : How likely your hypothesis is after considering the evidence.
• Likelihood : How probable the evidence is if your hypothesis were true.
• Prior probability : Your initial assumption or belief about the hypothesis.
• Marginal likelihood : The total probability of the evidence across all possibilities."

She then gives an example, with figures, and advice on how to think Bayesian: "New Belief = Old Belief + (Strength of Evidence × Confidence Adjustment)

• If the evidence strongly supports your belief, increase your confidence.
• If the evidence goes against your belief, decrease your confidence.
• If the evidence is weak or uncertain, make only a small adjustment."