In large data sets the Bayesian method gets a similar answer, but it’s not the same method. If you flip a coin once, and get heads, the frequentist method would say that the coin always lands on heads. The Bayesian method would never result in saying the coin always lands on heads unless it was assumed from the beginning.
In large data sets the Bayesian method gets a similar answer, but it’s not the same method. If you flip a coin once, and get heads, the frequentist method would say that the coin always lands on heads. The Bayesian method would never result in saying the coin always lands on heads unless it was assumed from the beginning.
I didn’t expect I’d end up saying this, but frequentists aren’t that naive either.
What does a frequentist do in this situation?
They won’t use that method when it gives results that absurd, but that’s still what the method says they should do.