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/
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Omega places a button in front of you. he promises that each press gives you an extra year of life, plus whatever your discounting factor is. If you walk away, the button is destroyed. Do you press the button forever and never leave?
Since I don't spend all my time inside avoiding every risk hoping for someone to find the cure to aging, I probably value a infinite life a large but finite amount times more than a year of life. This means that I must discount in such a way that after a finite number of button press Omega would need to grant me an infinite life span.
So I preform some Fermi calculations to obtain an upper bound on the number of button presses I need to obtain Immortality, press the button that often, then leave.