I would like to learn about advantages and disadvantages of transforming non-normally distributed data to achieve normal distribution versus using ranks and subsequent non-parametric tests. With assigning ranks to individual values, we lose some information. With transformation, we change the original distribution type. I need to know which of the two approaches is likely to return more accurate inferences about samples?

Would the following be correct?

If data can be transformed to a normal distribution, use parametric tests on transformed data.

If data cannot be transformed to a normal distribution, use non-parametric tests on raw data.

Thank you.

Igor