Prior probabilities and statistical significance
How does using priors affect the concept of statistical significance? The scientific convention is to use a 5% threshold for significance, no matter whether the hypothesis has been given a low or a high prior probability.
If we momentarily disregard the fact that there might be general methodological issues with using statistical significance, how does the use of priors specifically affect the appropriateness of using statistical significance?
Error
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!
bye bye nazi community
Er, what?
My guess is that this is a comment by the same user who posted the article. In a few minutes after the comment appeared (when I saw it), article’s Karma was at −1, and the usernames used to post both the article and the comment were deleted. Perhaps the user didn’t like the downvoting of their article and reacted by deleting their account.
It is. The other pages I had open still had the name visible, loldrup. Having a snit because of a −1 is unusual though. As the account is gone I can’t search their past postings, but I don’t remember them being egregiously wrong on LessWrong.