In contrast to independent measurements, matched data consist of measurements that should be considered together. For example, matching can be used in clinical studies. Here, patients that exhibit similar characteristics are paired in order to remove confounding effects. Matched data can also arise naturally when multiple measurements are performed on the same entity. For example, matched data can arise when a clinical marker is measured once before and once after a
treatment intervention. Irrespective of how the matched data were generated, their structure should be taken into account through the use of appropriate statistical tests.

Paired data

The most common type of matched data are paired measurements, which consist of two data points. For this type of data, the following significance tests are available:

Click on a variable type in the table to obtain more information on how to use the corresponding significance tests in R.

Repeated-measures data

If you have more than two matched measurements, then you are dealing with repeated-measures data. An example of a significance test that handles such data is repeated-measures one-way ANOVA.

Posts that deal with matched data

In the following posts, you can find more specific information on how you can handle matched data.

McNemar's test is a simple test for for checking whether pairwise measurements from two categories are independent. Here, I investigate the properties of the test and how it is used in R.

If you want to compare the means or medians of paired measurements, you can use a paired Student's t-test or a Wilcoxon signed rank test, respectively. This post explores the properties of these two tests and contrasts them.