Bayesian vs Frequentist

Heard of the ongoing debate in Statistics but never been sure what the fuss was all about? Here’s a quick run through to get you up to speed.

What is the debate?

For example, we have a coin and we flip it 100 times, getting 55 heads and 45 tails. We could write down our ‘model’ to be of a weighted coin, slightly bias towards heads.

Here is the problem: On the one hand, we have some data suggesting that this coin is biased towards heads. On the other hand, we know that 100 flips isn’t much, it could get closer to 50:50 if we keep flipping, and coins aren’t normally weighted (outside of boring maths textbooks). When deciding what our coin ‘model’ is, what should we care about most — the data or our prior knowledge of coins?

Frequentist Idea

Bayesian Idea

The difference

They often achieve the same results, but by looking at the problem from opposite ends!

Why does it matter?



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