They are different concepts, either you use statistical significance or you do Bayesian updating (ie. using priors):
If you are using a 5% threshold roughly speaking this means that you will accept a hypothesis if the chance of getting equally strong data if your hypothesis is false is 5% or less.
If you are doing Bayesian updating you start with a probability for how likely a statement is (this is your prior) and update based on how likely your data would be if your statement was true or false.
They are different concepts, either you use statistical significance or you do Bayesian updating (ie. using priors):
If you are using a 5% threshold roughly speaking this means that you will accept a hypothesis if the chance of getting equally strong data if your hypothesis is false is 5% or less.
If you are doing Bayesian updating you start with a probability for how likely a statement is (this is your prior) and update based on how likely your data would be if your statement was true or false.
here is an xkcd which highlights the difference: https://xkcd.com/1132/
Sweet!