Compare the variances of two datasets:

The -values are typically large when the variances are equal:

The -values are typically small when the variances are not equal:

Using Automatic applies the generally most powerful appropriate test:

The property "AutomaticTest" can be used to determine which test was chosen:

Compare the variances of many datasets simultaneously:

Compare the distributions of the datasets visually using SmoothHistogram:

Perform a particular test for equal variance:

Any number of tests can be performed simultaneously:

Perform all tests, appropriate to the data, simultaneously:

Use the property "AllTests" to identify which tests were used:

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 from a Levene test:

Extract any number of properties simultaneously:

The -value and test statistic from a Brown–Forsythe test:

Tabulate the results from a selection of tests:

A full table of all appropriate test results:

A table of selected test results:

Retrieve the entries from a test table for customized reporting:

The -values are above 0.05, so there is not enough evidence to reject normality at that level:

Tabulate -values for a test or group of tests:

The -value from the table:

A table of -values from all appropriate tests:

A table of -values from a subset of tests:

Report the test statistic from a test or group of tests:

The test statistic from the table:

A table of test statistics from all appropriate tests:

Set the significance level for diagnostic tests:

The default level is 0.05:

Setting the significance level may alter which test is automatically chosen:

A rank-based test would have been chosen by default:

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

Diagnostics can be controlled as a group using All or None:

Verify all assumptions:

Check no assumptions:

Diagnostics can be controlled independently:

Assume normality but check for symmetry:

Only check for normality:

Test assumption values can be explicitly set:

The Conover test was previously chosen because the data is not normally distributed: