Bayesian Updating
Continuously update your beliefs with new evidence.
Explanation
This approach, named after mathematician Thomas Bayes, means starting with an initial belief about something, then gradually updating that belief as you gather new evidence. The key is being willing to change your mind when the evidence changes, while weighing new information based on how reliable and relevant it is.
Real-World Example
Your coworker is usually reliable (prior: 90% reliable). They miss a deadline. Don't immediately think they're unreliable. Update slightly (now 85%). If they miss three more, update significantly (now 60%). One great delivery brings it back up. Your beliefs evolve with evidence, not binary switches.
How to Apply
Never be 0% or 100% certain—leave room to update. Write down predictions with probabilities. When wrong, examine why and adjust your model. Good updating: 'I thought 70% chance of success, failed, now I think 45% for similar projects.' Bad: 'I was totally wrong, I know nothing!' Update size should match evidence strength.