Regarding question 1) You would need to test it in both groups separately. If you test it on the combined data, then you will be picking up treatment effects. If there is a big difference between control and treatment groups, then the distribution of the combined data will most certainly not be Gaussian (it will be bimodal).

A better method than looking at the raw data is to examine the residuals of the model (Google "residual analysis"). If you have two groups, then you will have to do two tests of significance. Then do you correct for multiple testing, and if so, which test? P-values and adjusted p-values can change depending on your decisions. This gets much worse when you have more groups.

It is also helpful to plot your residuals (histogram and Q-Q plot: http://en.wikipedia.org/wiki/Q-Q_plot) rather than relying only on a p-value. Remember: very small deviations from normality will be statistically significant with a large sample size, but are not relevant for inferences from the analysis, since many common tests are robust to small deviations from normality.