Perhaps it is too late to join the conversation, but I'll try anyway.
I agree with the comment regarding the assumption of normality. In this case, a normality test based on such a small sample will likely fail to reject the null hypothesis simply because there isn't enough data to say otherwise. But for fun, I researched and applied several different tests for normality using Excel (Geary test, Chi Sq Goodness of Fit, and Chi Sq Normality Test), all of which returned high p-values indicating one would fail to reject the null hypothesis and move forward with the assumption of normality.
For the marketing question - Was a Marketing Campaign Successful? My null hypothesis was one of no change; there was no difference in sales as a result of the marketing campaign. My alternative hypothesis was that the sales prior to the marketing campaign were less than the sales experienced during the marketing campaign. My thought is that I would not want the continued expense associated with a marketing campaign if it did not result in higher sales.
I conducted an F-test which returned a p-value larger than 0.05, so I fail to reject the null hypothesis and conclude that the variances are equal.
Now, I conduct the t-Test which returned a p-value larger than 0.05 (0.0889), so I fail to reject the null hypothesis and conclude that there was no difference in sales as a result of the marketing campaign.
So, rather than continue to spend money on this campaign, I'll allocate it to other marketing activities. :-)