Independent T-test, paired T-Test and One-Sample T-test

Input:

Input Format: the expected input format is each sample in a different column.

When choosing T-Test in from the “Analyze” Menu, a specialized T-test menu will open up with options for three types of T-tests:

• Independent – This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances by default. (Note: it is possible to remove this assumption by either tweaking the resulting notebook, or contacting the developers).

• Paired – This is a two-sided test for the null hypothesis that 2 related or repeated samples have identical average (expected) values.

• One Sample – This is a two-sided test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean. This option requires an extra input of an expected average value to compare to:

Once you’ve selected the desired T-test and (if required) sample average, click ‘Run’. The results will typically take 5-10 seconds to appear while the input is propagated into a Jupyter framework and the results are rendered back.

Output:

An example of an Independent T-test on sample data is shown below, where only particular output cells from the original Jupyter notebook are rendered. For the full notebook, please see the links below either for downloading the full notebook, or viewing the it in html format.

Viewing the full notebook in html: