Paired T-test

      Paired T-test


        Article summary

        The standard T-test assumes that all samples are drawn at random from a population. But this may not be the case.

        For example, you might select test subjects at random, but then perform two measurements on each subject, for example both before and after a medical treatment.

        This is called a Paired T-test (or a dependent sample T-test).

        This pairing makes the test more powerful than the standard T-test, as there is no random variation between subjects - the focus can be on the difference measured for each subject.

        The 'Identifier' variable type
        The Paired T-test is unique in that it uses a variable type called 'Identifier' for the column with the variable that identifies the subject.

        How to perform a Paired T-test

        Your data table will need three variables, as compared to two for the standard T-test:

        • The Numeric Dependent variable (value being measured)
        • A Categorical Independent Variable (grouping the samples into the two groups being compared)
        • An Identifier variable, which for each sample identifies which test subject has been measured

        These can then be visualized as a dot plot in one of two ways, either with the Identifier as the Z axis:

        Or with the Identifier as the X axis:

        Both of these visualizations can give you an idea of whether there is a visible effect of the treatment.

        Running the test

        Then you can run a Graph-Driven Paired T-test on the data. See how to do this here.

        You can also run this test directly from the Hypothesis test option on the left-hand menu as described here.



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