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Statistical tests used to compare sets of data have been designed for data sets that are either paired or unpaired, making it important to use the correct test to prevent erroneous results. Tests for paired data include McNemar's test and the paired permutation test. Tests for unpaired data include Pearson's chi-squared test and Fisher's exact ...
Others compare two or more paired or unpaired samples. Unpaired samples are also called independent samples. Paired samples are also called dependent. Finally, there are some statistical tests that perform analysis of relationship between multiple variables like regression. [1] Number of samples: The number of samples of data.
A paired difference test, better known as a paired comparison, is a type of location test that is used when comparing two sets of paired measurements to assess whether their population means differ. A paired difference test is designed for situations where there is dependence between pairs of measurements (in which case a test designed for ...
For partially paired data, the classical independent t-tests may give invalid results as the test statistic might not follow a t distribution, while the dependent t-test is sub-optimal as it discards the unpaired data. [21] Most two-sample t-tests are robust to all but large deviations from the assumptions. [22]
For two matched samples, it is a paired difference test like the paired Student's t-test (also known as the "t-test for matched pairs" or "t-test for dependent samples"). The Wilcoxon test is a good alternative to the t-test when the normal distribution of the differences between paired individuals cannot be assumed. Instead, it assumes a ...
The sign test is a statistical test for consistent differences between pairs of observations, such as the weight of subjects before and after treatment. Given pairs of observations (such as weight pre- and post-treatment) for each subject, the sign test determines if one member of the pair (such as pre-treatment) tends to be greater than (or less than) the other member of the pair (such as ...
To exploit variance reduction with paired samples, a paired permutation test must be applied, see paired difference test. This is equivalent to performing a normal, unpaired permutation test, but restricting the set of valid permutations to only those which respect the paired nature of the data by forbidding both halves of any pair from being ...
Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [4]