Set the known variance to 1:

Setting the known variance to Automatic is equivalent to using the sample variance:

The result is the same:

Specify a single common variance for two groups, or give a list of variances:

For multivariate data, covariance matrices can be given:

A single group covariance:

Separate covariance matrices for each dataset:

Test versus :

The -values are typically large when the mean is close to :

The -values are typically small when the location is far from :

Using Automatic is equivalent to testing for a mean of zero:

Test versus :

The -values are typically large when the mean is close to μ₀:

The -values are typically small when the location is far from μ₀:

Test whether the mean vector of a multivariate population is the zero vector:

Alternatively, test against {0.1,0,-0.05,0}:

Test versus :

The -values are generally small when the locations are not equal:

The -values are generally large when the locations are equal:

Test versus :

The order of the datasets affects the test results:

Test whether the mean difference vector of two multivariate populations is the zero vector:

Alternatively, test against {1,0,-1,0}:

Create a HypothesisTestData object for repeated property extraction:

The properties available for extraction:

Extract some properties from a HypothesisTestData object:

The -value and test statistic:

Extract any number of properties simultaneously:

The -value and test statistic:

Tabulate the test results:

Retrieve the entries from a test table for customized reporting:

Tabulate -values or test statistics:

The -value from the table:

The test statistic from the table:

A two-sided test is performed by default:

Test versus :

Perform a two-sided test or a one-sided alternative:

Test versus :

Test versus :

Test versus :

Perform tests with one-sided alternatives when μ₀ is given:

Test versus :

Test versus :

Set the significance level for diagnostic tests:

By default, 0.05 is used:

The significance level is also used for "TestConclusion" and "ShortTestConclusion":

Control whether to verify the assumption of normality:

Assume normality:

Check for normality:

Alternatively:

Unlisted assumptions are not tested:

Normality is assumed:

The result is the same but a warning is issued:

Bypassing diagnostic tests can save compute time:

It is often useful to bypass diagnostic tests for simulation purposes:

The assumptions of the test hold by design, so a great deal of time can be saved:

The results are identical: