Let's take the Perfume example for which we have defined five segments with the Breakout Variable Product, namely Prod3, Prod4, ProdG1, ProdG5 and Prod G6.
Below is the table with the Standardized Total Effects of all the observable variables on Purchase Intent, and the mean values of these variables, computed on the entire data set, and on the segment represented by Prod3. These figures allows computing the Impacts of each variable.
Null Value Assessment
The default Confidence Level has been set to 95%. This is the same for the both tests.
As for the Bayesian test, the Region of Practical Equivalence (ROPE) on the Effect size around the null value has been set by default to [-0.1, 0.1].
The null value is declared to be rejected if the 95% Highest Density Interval (HDI) falls completely outside the ROPE.
You can use Window | Preferences | Tools | Statistical Tools the Preferences to modify:
Checking Null Value Assessment triggers the computation of both tests.
When the mean values are estimated as significantly different, a square is added next to the Impact bar: