As marketers, when we’re conducting research surveys, we want to get the most information out of our data. In doing so, we need to make sure that the results are analyzed properly. Just by simply looking at the numbers won’t give us the in-depth analysis that we need to make the best marketing decision for our clients. So, we dig deeper, we run statistical test such as a T- Test to determine if there is an actual statistical significance. If the results of the T-test indicates that the p-value equals .05 or less, there is a significant difference. If the P value equals .051 or higher, then the results are not significant. For instance, after conducting a survey we received the following results: For question one (How many hours do you work per week) we want to compare the means from two subgroups (males and females). The results show that males work an average of 38 hours per week and females work 41 hours per week. Even though the numbers are different, is there an actually statistical difference? After conducting a T test, the p value = 0.25. Since the p value is higher than 0.5, there is not a significant