Desktop User Guides > Professional > Table scripting > Statistical tests

Statistical tests
You can run statistical tests on tables to show whether differences in the distribution of counts in tables are statistically significant, or they are due to chance.
Name
Description
This test tests whether the variables on the side and the top of a table are independent.
For example, it can be used to show whether or not variations in political opinions depend on the respondent's age.
This test tests whether each table cell is significantly different from its expected value in the overall table.
This testlooks at the rows of a table independently and compares pairs of columns, testing whether the proportion of respondents in one column is significantly different from the proportion in the other column. The proportion is the count in the cell divided by the base for the column.
This test looks at means that are presented in a row of a table and compares pairs of columns, testing whether the mean in one column is significantly different from the mean in the other column.
This test looks at the variables on the side and top of a table with two rows and two columns and tests whether they are independent. It is suitable for use in a subset of the tables for which the chi-square test is available.
This test deals with each row independently and compares the proportions in four columns at a time to test whether the difference between the values in the first pair of columns is significantly different from the difference between the values in the second pair of columns.
This test deals with each column independently and compares pairs of rows to see whether the figures in each pair differ significantly from one another.
This test applies the column proportions or column means test to a combination of variables that you select, to provide a breakdown of results by combinations of individual categories within the selected variables.
This test compares the means of two variables, computes the difference between the two variables for each case, and tests to see if the average difference is significantly different from zero.
This test tests the significance of unplanned pairwise comparisons.