# JASP BayesFactor interpretation for mixed anova interaction

Hi

I am using JASP to analyze the data and bayesfactor of mixed ANOVA, but I got some problem with interpretation.

It's a 3(between)x6(within) ANOVA design. The main effect of within variable(A) is significant, but the main effect of the between variable(B) and interaction( AxB ) are not significant. In the bayesfactor(compare to null model), the BF10 are:

A: BF10 = 1.59e+23

B: BF10 = 0.423

A + B: BF10 = 7.56e+21

A + B + A*B: BF10 = 7.02e+21

Is that I can sure there is a strong evidence for the main effects model and interaction model?

The ANOVA p value is non-significant, I have no idea how to interpret the combine result...

Is the BF10 is as bigger as possible in compare to null model order, and as smaller as possible in compare to best model? I am so confused in these detail...

Thank you very much.

## Comments

Hi Herry,

In sum, you need only A. Adding anything else makes the model worse, predictively.

Cheers,

E.J.

Hi EJ

I get it! Thank you so much. Just like the hierarchical regression concept. Thank you so much for the suggestion. I try select compare to best model, the BF10 is more clear!

A: BF10 = 1

A + B: BF10 = 0.523

A + B + A*B: BF10 = 0.466

Null model(incl. subject) = 7.562e-23

B: BF10 = 0.423 = 3.465e-23

Are these result stronger shows that no model is stronger than A?

So if that means in the result, although the interaction is significant in ANOVA, the BF10 is not strong, is that means the data is also not fit to the theory...? I am confused when p-value and the Bayesian result are opposite, how can I make the choice between p-value and Bayesian...

Thank you

E.J.