Practitioners will be quick to accept t-tests between respondent groups for ACBC by converting the utilities to zero-centered diffs (to normalize, equalize the scale)...then compute a regular t-test. However, this is not very acceptable for formal statistics or formal journal reviewers.
Bayesians would prefer to estimate ACBC utilities under HB estimation with the groups as covariates, then open the alpha.csv file that records the draws of the alpha vector. Using only the iterations after convergence is assumed, they would count the % of draws for which one group of respondents is higher than another group of respondents on the estimate of alpha for a given utility level.
If we were referring to utilities for a quantitative attribute (like price or speed), it might be advisable to estimate a single linear parameter for the attribute to capture its slope. Then, the Bayesian test would only need to consider the difference in slope between covariate groups by examining the 1000s of draws of alpha after convergence.
I would think this Bayesian approach would be defensible for publishing in technical journals.
For more info on covariates in HB, see our white paper: http://www.sawtoothsoftware.com/downloadPDF.php?file=HBCovariates.pdf