There is a variant of the t-test that can be used when the data is paired (this usually happens when we have two observations for each subject). For example, this may occur if we use a specific program and we want to evaluate its effectiveness by taking one measurement before and after the program is executed. The advantage of this is that the difference (after-before) truly represents the impact of the program that we are evaluating (we are making sure that any possible external variable has been filtered out).
This is much better than having just two samples, where one is taken before the policy was executed and another is one (with different individuals) taken after. If there are differences between those two samples, they might bring trouble to our test. For example, let's suppose that sample1 contains individuals that perform better at work, while sample2...
