🔍 Bayes’ Theorem: the art of updating beliefs with new data
In data science, few tools are as powerful as Bayes’ Theorem.
📌 What does it do? It lets us update the probability that something is true when we receive new evidence.
🧠 Imagine a very accurate medical test. Even so, if the disease is rare, a positive result doesn’t automatically mean you’re sick. This happens because of missing context about prevalence.
➡️ This is called the base rate fallacy.
💡 Key idea:
It’s not enough to look at the test result; you must consider how common the event is in the population.
🚀 Where we use it in practice:
- 🧪 A/B testing
- 📬 Spam filters
- 💳 Fraud detection
- 🔁 Any system that learns and adjusts predictions with new information
Bayes is not just a formula: it’s a way of thinking based on evidence.
Also published on LinkedIn.
