Is this how JASP defines H0, H1, calculates the BF and plots the results ?
Can someone please confirm if my understanding is right? Or - correct me if I am wrong? Thanks!
I am doing a One Sample Bayesian t-test. Data: 175 cases, sample mean for variable V is 6.59
H0: population mean is 6.0
H1: population mean > 6.0
(in other words, I am testing the hypothesis that δ>0 versus δ=0).
HOW IS H0 SPECIFIED? Is H0 a normal distribution, centered on δ=0 ? How is the spread specified (is the standarddeviation of this H0-distribution somehow derived from the _sample _standard deviation ?? That would be strange, because then you inject information from the data into the H0, thus improving the fit between H0 and data...... ?
Am I correct in that H1 is the positive portion of the Cauchy distribution, with most mass close to zero. ?
My BF10 = P(data|H1) / P(data|H0) So this is a ratio of two p-values, right?
In my case BF10 = 23915, suggesting that the data are more supportive of H1 than of H0. But what does the "error %" mean, that is given BF? In my case it is 1.253 e-10 (so very small).
Thanks ! Learning every day... !