# Custom Tables: Test Statistics Tab

This feature requires the Custom Tables option.

The Test Statistics tab provides significance tests for custom tables.

These tests are not available for tables in which category labels are moved out of their default table dimension or for computed categories.

- Column Means and Column Proportions Tests
- Column means tests are available for scale variables. Column proportions tests are available for
categorical variables.
- Compare column means
- Pairwise tests of the equality of column means. The table must have a categorical variable in
the columns and a scale variable as the innermost level of the rows. The table must include the
mean as a summary statistic.
For ordinary categorical variables, the variance can be estimated from all categories or from just the categories that are compared. For multiple response variables, the variance for the means test is always based on just the categories that are compared.

- Compare column proportions
- Pairwise tests of the equality of column proportions. The table must have at least one categorical variable in both the columns and rows. The table must include counts or column percentages.

- Identify Significant Differences
- For column means and column proportions tests, you can display significant results in a separate
table or in the main table.
- In a separate table
- Significance tests results are displayed in a separate table. If two values are significantly
different, the cell corresponding to the larger value displays a key that identifies the column with
the smaller value.
- Display significance values
- The significance values are displayed in parentheses after each key value in the cell. This option is available only when significant results are displayed in a separate table.

- In the main table
- Significance test results are displayed in the main table. Each column category in the table is
identified with an alphabetic key. For each significant pair, the key of the category with the
smaller column mean or proportion appears in the category with the larger column mean or proportion.
- When you hover over a key in the column label cell in a pivot table, all cells in the table with that significance key are highlighted. For a table with multiple variables in the column dimension, only cells in that sub-table are highlighted.
- To select all cells in a table (or sub-table) that have the same significance key, right-click on the column label cell and choose .

- Use APA-style subscripts
- Identify significant differences with APA-style formatting that uses subscript letters. If two values are significantly different, those values display different subscript letters. These subscripts are not footnotes. When this option is in effect, the defined footnote style in the current TableLook is overridden and footnotes are displayed as superscript numbers. To select all cells in the same row with the same significance key, right-click on a cell that has a significance key and choose

- Significance levels
- The significance level for column means and column proportions tests.
- The value must be greater than 0 and less than 1.
- If you specify two significance levels, capital letters are used to identify significance values less than or equal to the smaller level. Lower case letters are used to identify significance values less than or equal to the larger level.
- If you select Use APA-style subscripts, the second value is ignored.

- Adjust p-values for multiple comparisons
- The Bonferroni correction adjusts for the family-wise error rate (FWER). The Benjamini-Hochberg method is a false discovery rate (FDR) adjustment. This method is less conservative than the Bonferroni correction.
- Tests of independence (Chi-square)
- Chi-square test of independence for tables in which at least one category variable exists in both the rows and columns.
- Use subtotals in place of subtotaled categories
- Each subtotal replaces its categories for significance testing. Otherwise, only subtotals for which the subtotaled categories are hidden replace their categories for testing.
- Include multiple response variables in tests
- Categories of multiple response sets are included in significance tests. Otherwise, multiple response sets are not included in significance tests.