Bayes factor calculator: BF10 and BF01
P(data | H1) = the probability of getting the data, given that the   alternative   hypothesis is true
P(data | H0) = the probability of getting the data, given that the   null   hypothesis is true
Prior-posterior plots and Bayes factors
1. Change the values in the menus above and note how the Bayes factors change. What produces a large Bayes factor? What produces a small Bayes factor?
2. Using the menu below, select several different Bayes factors (BF10), and note how the curve changes.
3. What does a BF10 of 1 mean? Why does the curve fall on the diagonal when the Bayes factor is 1?
4. What does a BF10 of 10 mean? Why does the curve bend above the diagonal when the Bayes factor is 10?
5. For a BF10 of 4, the posterior is .80 when the prior = .50. Why? (Hint: 1/1 odds = probability of 1/2 or .50).
6. For a BF10 of 4, what is the posterior when the prior = .20? (Hint: 1/4 odds = probability of 1/5 or .20).
7. Why does the curve drop below the diagonal when BF10 = .50?
8. Suppose you did a study to test a new persuasion technique. Before the study (based on theory and previous research) you believe that there is a 30% chance that the new technique will work. The results of the study support the idea that the technique works, and the Bayes factor equals 10. Now that you know the results of the study, how sure are you that the technique works?