 In the other videos, you did tests for gender differences. Those are independent samples tests, because no person can belong to both genders at once. Sometimes, though, you have experiments where each person gets multiple scores on a variable. For example, you might give each person a pre-test and post-test to see if a teaching method makes a difference in scores. Or, you might give a person a reaction time test after drinking coffee, then another reaction time test after drinking decaf to see if caffeine has any effect on reaction time. These are called repeated measures or paired measures. Since the survey didn't have any repeated measures, I found some before and after data. The weights of amateur wrestlers at the first tournament and the last tournament they competed in during a season. A t-test will let you find out if, on the average, a competitor's weight increases, decreases, or stays the same. In this case, my hypothesis was the same as the null hypothesis, that there would be no significant difference between the before and after weights. In SOFA, choose statistics, then t-test paired, choose the table you need, and the groups, in this case before and after, and show results. As you can see, the probability value is 0.488, which is well above 0.05, so there is no significant difference between the before and after weights. In fact, if you look at the means and standard deviations, you'll see that they're almost identical, and you'll see that most of the differences are centered around 0.