When comparing two sets of data, you subtract one from the other to find the difference for every group in the set. These differences are the means of paired data. This is called the Paired T-Test. After finding the mean differences, we want to know how close to the true mean we are for the matched pairs. A Paired T-Interval constructs a confidence interval to estimate the mean difference between matched pairs of data. This confidence level tells us how far away we are from the true mean.

Before we can create a confidence interval, we need to check our conditions:

Paired Data Condition - the data must be paired.

Independence Assumption - if the data are paired, the groups are not independent. Rather, the differences must be independent of each other.

Randomization Condition - the groups must be random.

Nearly Normal Condition - assume the population of differences follows a Normal Model. We can check this condition with a histogram.