Let's say we are measuring the amount of anger (numerically) among males and females before and after watching a short video.
|Males||Anger (before video)||Anger (after video)|
Imagine we do the same for females (see the table below).
|Females||Anger (before video)||Anger (after video)|
1st t-test (males): I can use a t-test on the 1st table (males) to determine whether the two columns are significantly different, i.e. did the video make them angry.
1st t-test (females): Exactly the same except using the 2nd table (females).
Conclusions: Let's pretend that both males & females had significantly higher levels of anger after watching the short video.
Question: The short video gets males and females angry. More specifically, the males got more angry by an amount (respectively) of 4,13, and 7. The females have similarly calculated differences. My question is whether Males got more angry than females from watching the short video. So, can I perform a t-test on the two columns below (the difference columns)?
|Difference (male)||Difference (female)|
A significant result would mean that one of the sexes was affected more by the video. A non-significant result would mean that they both were affected about equal. (I know that I am simplifying significance a little bit here)