Most appropriate statistical test of significance when comparing two groups of awareness data I have 2 sets of brand preference data (in %) to measure impact of online advertising on target group F20-40 years i.e., 1.different time period (pre/post - not same consumers but same target group) & 2. Control (not seen online advertising) vs Exposed (exposed to advertising).  Could you suggest what statistical significance test I should use and why?
 A: If you assume that the preference is a normally distributed random variable, you can use a two sample t-test. It will test whether the null hypothesis that data from two groups are independent random samples from normal distributions with equal means and equal but unknown variances, against the alternative that the means are not equal.
You can use ttest2 in MATLAB. 
A: If I fully understand your problem, I would try a difference-in-differences approach. The basic idea is this (though in your case, I do hope the lines slope up):
http://ec.europa.eu/regional_policy/sources/docgener/evaluation/evalsed/sourcebooks/method_techniques/images/diff-in-diff_post_and_pre-program.gif 
You can estimate the effect with a simple regression of brand preference on three variables and any other covariates that you may have collected. You want to test that the interaction coefficient is greater than zero.
A: You could treat this as a  2 by 2 design. Variable 1 is exposed/not and variable 2 is pre/post.  People are nested in Variable 1 (meaning that each person gives you both pre and post information for either exposed or not exposed conditions).  You could analyze this with a 2-way analysis of variance (ANOVA) with nesting. This is a somewhat sophisticated analysis, so you may not have run into it before.  A simpler approach would be to compute a pre-post difference score and then do an independent groups t test on the difference scores, comparing exposed versus not exposed groups.  This simpler approach should serve your purpose.  Of course, you need to think about meeting the various assumptions of the t-test (or ANOVA).
