A common misconception about Bayesian statistics is that it mainly involves incorporating personal prior beliefs or subjective opinions.

While priors do play a role, the core strength of Bayesian statistics lies in its ability to update knowledge as new data becomes available – a significant advantage over traditional frequentist methods.

Frequentist statistics rely on fixed probabilities based on large datasets, often assuming that data doesn’t change over time. This approach can be limiting when dealing with new, evolving data.

Bayesian statistics, on the other hand, combines prior knowledge (priors) with new data (likelihood) to continuously update and improve predictions, creating a dynamic, adaptable framework.

Take cancer diagnosis as an example. In cell classification, doctors use complex algorithms to analyse tissue samples and classify cells as benign or malignant. Initial assumptions, based on prior knowledge like patient history, might suggest certain probabilities.

But as new test results are collected, Bayesian statistics update these probabilities. By refining predictions with new evidence, it helps improve diagnostic accuracy.

This ability to continuously adapt is what makes Bayesian statistics so powerful, especially in fields like healthcare. It’s not about starting with subjective opinions but about continuously refining predictions as new evidence emerges.

This dynamic approach ensures better, data-driven decisions, ultimately leading to more accurate diagnosis and treatments, and offering clear advantages over traditional statistical methods.