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Maybe you would find it useful to read a textbook on bayesian stats for inspiration. I can recommend Richard McElreath's "Statistical Rethinking" which makes it very clear how inflexible it is to just know recipes like t-tests or anovas.
The canonical approach is to build a generative model with a parameter (or multiple for ~anova) that codes for the difference between groups and do inference on that parameter of interest. Most of the recipes taught in statistics classes can be modelled as a regression of some kind (this counts for frequentist stats too, see https://lindeloev.github.io/tests-as-linear/ ). Some advocate to do that inference with bayes factors. Others, like discussed elsewhere in this thread, advocate combining the resulting posterior with a cost/value function, but either way the lesson is that there is less focus on "t-test-vs-anova" because they're the same thing anyways.
Thanks for the "Statistical Rethinking" recommendation. I had watched some of McElreath's lectures so I knew of the book, but next year I think I'm finally going to read it from end to end and follow along the 2024 course schedule: https://github.com/rmcelreath/stat_rethinking_2024#calendar-...
I had previously started the BDA course, which is another famous Bayesian course, see https://avehtari.github.io/BDA_course_Aalto/ but I didn't finish it due to travel.
No more excuses in 2024... time to level-up the Bayesian modelling skill ;)