Is this how JASP defines H0, H1, calculates the BF and plots the results ?

Comments

• EJ

  September 2016

  Hi Pieter,

  The answers:
  1. "HOW IS H0 SPECIFIED?"

  H0 is the single point delta=0. So there is no spread. H0 states that the effect is completely and utterly absent.

  2. "Am I correct in that H1 is the positive portion of the Cauchy distribution, with most mass close to zero?"

  Yes. It is obtained by folding the bell-shaped two-sided Cauchy distribution around 0.

  3. "My BF10 = P(data|H1) / P(data|H0) So this is a ratio of two p-values, right?"

  Not quite. For instance, the p-value is not the probability of the data, but the probability of encountering a test statistic at least as extreme as the one you've observed. There are other differences as well, but basically, the p-value is a tail area integral. The Bayes factor is a (generalized form of) likelihood ratio.

  4. "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)."

  "Suggesting" is rather too modest -- the data provide overwhelming support and are 23,915 times more likely under H1 than under H0! The error percentage is there because the result requires some numerical integration. I forgot what it does exactly...let me check.

  Cheers,
  E.J.
• pieter

  September 2016

  Thank you EJ. Guess I will dive into some papers on BF to learn.more.