Applying statistical tests

UNICOM Intelligence Reporter provides some statistical tests that you can run on your tables. Use these tests to show whether differences in the distribution of counts in tables are statistically significant or whether they are merely due to chance. The following table lists the tests that are available. Each test has a number of requirements regarding the structure and contents of the tables on which it can be performed.

Name	Description
Chi-square test	This test looks at the variables on the side and the top of a table and tests whether they are independent. For example, it can be used to show whether or not variations in political opinions depend on the respondent's age.
Column proportions test	This test looks 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.
Column means test	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.
Fisher’s exact test	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.
Net difference test	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.
Paired preference test	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.
Product difference test	This is not a separate statistical test in its own right; instead, it applies the column proportions or column means test to a combination of variables that you select, to provide a detailed breakdown of results by combinations of individual categories within the selected variables.
T-test test	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.

	Chi-square/Fisher	Column proportions (/prod diff)	Column means (/prod diff)	Net difference	Paired Preference
SigLevel	No	Yes	Yes	Yes	Yes
SigLevelLow	No	Yes	Yes	Yes	Yes
MinBase	No	Yes	Yes	Yes	Yes
SmallBase	No	Yes	Yes	Yes	Yes
ShowLSD	No	Yes	Yes	No	No
UseQFormula	No	Yes	Yes	No	No
UseContinuity Correction	No	Yes	No	No	No