Define a matched pairs design.
State examples of statistical questions where a matched pairs design would be appropriate.
Describe the setup of a data set collected from a matched pairs design.
State the sampling distribution of the average difference score from a matched pairs design when we assume the population of difference scores is Normally distributed.
State the \(T\)-statistic used in a matched pairs design.
State the sampling distribution of the \(T\)-statistic used in a matched pairs design when we assume the population of difference scores is Normally distributed.
Perform a hypothesis test for a population difference from data fitting a matched pairs design using the appropriate matched pairs \(t\)-test.
Construct a confidence interval for a population difference from data fitting a matched pairs design using the appropriate matched pairs \(t\)-test.
Use one.sample.t.test to test a hypothesis or construct a confidence interval using summary data from a matched pairs design.
Give reasons why we must carefully identify whether an independent samples \(t\)-test or matched pairs \(t\)-test is most appropriate for analyzing a data set.

Explain why we should check for the Normality of an underlying population before performing any of the \(t\)-tests or constructing any of the \(t\)-based confidence intervals we have developed so far in the class.
Construct a density plot in R from a data set, and diagnose whether the density plot indicates any clear departures from Normality in the underlying population.
Explain, loosely, what a Q-Q plot is showing, and what a “good” and “bad” Q-Q plot looks like.
Construct a Q-Q plot in R from a data set, and diagnose whether the Q-Q plot indicates any clear departures from Normality in the underlying population.
Given a Q-Q plot, identify whether the Q-Q plot points towards:
- Normality
- Left-skewness
- Right-skewness
- Heavy tails
Identify which property of a population, overall, is most problematic to \(t\)-based inferential procedures.