A t-test is a statistical hypothesis test in which the test statistic obeys a Student's t-distribution under the null hypothesis. It can be used to test whether two datasets are significantly different from each other. A t-test is mostly applied when the values of a scaling term in the test statistic are known and follow a random distribution pattern. When the scaling term is unknown and is replaced by an estimate according to the data, the test statistics (under some conditions) follow a Student's t distribution.
Two-sample t-tests for a difference in mean include independent samples or unpaired samples. The independent samples t-test is applied when two separate sets of independent and identically distributed samples are available, one from each of the two populations being compared. Paired samples t-test is typically composed of a sample of matched pairs of similar units, or one group of units that has been tested twice (a "repeated measures" t-test). Paired t-tests are a type of blocking, and is more accurate than unpaired tests when the paired units are similar with respect to "noise factors". In a different context, paired t-tests can be applied to reduce the influence of confounding factors in an observational study.
Below shows the commonly used t-tests:
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