| Pro | Con |
---|---|---|
Interval BFs | + Influenced more moderately by varying equivalence regions R, compare the spread in power between different choices of R in Fig. 2 (for example, power ranges between 60% and 80% for small effects) | – Less robust to the prior selection (for example, compare the difference in power depending on the selected prior in Fig. 2) |
 | + Recommended in situations with little uncertainty about the prior selection but limited knowledge how to choose the size of the equivalence region | – The OH model may be attractive in some situations but yields very large error rates, making it practically unusable |
 | + Reliable type I error control for small sample sizes |  |
ROPEs | + Robust to the prior selection (see the horizontal progression in Figs. 3, 4 and 5) | – Influenced stronger by varying equivalence regions R, compare the spread in power between different choices of R in Figs. 3 and 4 (for example, power ranges from 50% to 80% for small effects and medium prior) |
 | + Recommended in situations where the equivalence region is motivated from subject-domain knowledge or pilot studies but there is considerable uncertainty about the prior | – Only the full ROPE controls type I errors for small sample sizes |
 | + The full ROPE yields the best type I error control which is important if the stakes of a false-positive result are high |  |