Box-Cox, log and arcsine transformations have the aim of make the data more Normal. My question is: how can I choose between each one of these transformations? Which assumptions do I need to have to do that?
Normally I see people trying these transformations when modeling and simply picking the one which better performs - without any strong assumption or theory behind that.
I'm not so versatile in statistics but I'm trying to further understand it.
