Does data tranformation result in normal distribution? I have seen a question  that claims transformation  changes a distribution into a normal distribution. Is  this a correct assertion?
 A: No. There are a lot of cases where a particular transformation may make the transformed distribution closer to normal, like using taking the square root of data to address rightward skew. But usually when transforming data this way the goal is to get a result that is "normal enough" for various statistical assumptions to hold (or come close enough to holding).
Other transformations are done to better model true relationships among data elements. If two variables have a logarithmic relationship but the unit of measure does not reflect that, then a log transformation may make that relationship more clear, easier to work with, and more interpretable, whether the underlying distribution is normal or not (either before or after transformation).
It is definitely not correct to say that transformations, in general, change any non-normal distributions into normal distributions.
A: It depends on the underlying starting distribution. Many transformations can make skewed data, into a more "normal looking" type of data. Often "Log" transformation is used to that purpose (as it can compress heavily skewed data). The Box-Cox transformations are also sometimes used. 
A: In order to convince you that the question is correct suppose that $x\sim N(0,1)$ is standard normal value and $y=sign(x)*x^2$ is another random value. So now it is clear that $y$ is not normaly distributed. However taking inverse transformation gives $x=\sqrt{|y|}*sign(y)\sim N(0,1)$. That is, transformation of $y$ changes its distribution to normal.
