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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.

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