Matched Pairs Design vs Experiment
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Matched pairs design
Collecting data and study design
A matched pairs design compares two treatments within pairs of similar units, or one unit measured twice, then analyzes the difference inside each pair.
A matched pairs design controls for variation between units by comparing treatments inside pairs that are alike, or on the same unit under both conditions. Because each pair serves as its own comparison, differences between individuals cancel out and the test focuses on the within-pair difference. For example, you measure each runner's time in old shoes and new shoes, then analyze the 20 time differences. The analysis is a one-sample procedure on the paired differences, using , where is the mean difference, its standard deviation, and the number of pairs.
Experiment
Collecting data and study design
An experiment imposes treatments on subjects and compares their responses, supporting a cause-and-effect conclusion when treatments are assigned at random.
In an experiment the researcher actively assigns treatments rather than just observing what happens. For example, randomly assigning patients to take a new drug or a placebo and then comparing recovery rates is an experiment. Random assignment balances out other variables across the groups, which is what lets a difference in response be attributed to the treatment. Its key ideas are comparison, random assignment, and replication across many subjects.