# Parametric _ Non parametric

My scale's data is normally distributed, except for one dimension of the scale... The overall data has a normal distribution along with several dimensions in the scale.

First, suppose that all data are normally distributed, can I use non-parametric test? Is there a rule which requires the use of parametric tests when data is normally distributed. I know parametric is more powerful but is it a problem to insist on non-parametric test?

Second, while deciding for the types of tests, should I check total scale's k-s values for deciding whether data is normally distributed or... first total, then dimensions one by one... what should one do if all dimensions are normally distributed except for one dimension?

Thanks

• 1. None of your scales are actually normal. It's pointless to test it (but if you must test, why use one with such low power in the tails?). 2. you can have parametric non-normal methods. 3. non-parametric is not the same as non-normal; you can use non-parametric methods on anything for which the assumptions would be reasonable. However, all of these questions are answered on site already. – Glen_b Nov 2 '17 at 0:21
• See for example -- stats.stackexchange.com/questions/41764/… and stats.stackexchange.com/questions/266586/… which relate to several of the issues here – Glen_b Nov 2 '17 at 0:51