If, for example, we were interested in how attitudes toward homosexuals might change after contact with a homosexual, and we had data that measured attitudes before (pre) and after (post) contact, then the paired t-test would be the statistical technique of choice.

It is extremely important to distinguish clearly between paired and independent means t-tests as each test rests on different assumptions. The paired test assumes that the variables of interest are correlated. In this manner each individual's score on 1 variable (e.g. pre-test) is compared to their own scores on the other variable (e.g. post-test). In this manner, the paired test takes into consideration each individual's change in score. Applying this test incorrectly to unpaired data can lead to errors as scores that are unrelated are incorrectly treated as pairs.

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