Expands the Capabilities of SPSS and Excel 

Program Dynamic

When we conduct many statistical tests, a common concern is that of an increased probability of chance findings across the tests.  Strategies have been developed to control the error rate across a set of contrasts.  A popular approach is one commonly called the Bonferroni method.  This program provides you with information you need to do a Bonferroni correction as well as three popular alternatives to it (described below in conjunction with the ZumaStat output). 

How it Appears on Your Screen

The Output

The output appears as follows:

ZumaStat first prints out the input data so that if you print the output, you have a record of what you input.

ZumaStat then provides the traditional Bonferroni critical p value, which is the per comparison alpha level divided by the number of contrasts.  Statisticians have shown that this test tends to be too conservative and that modifications to the approach can effectively control experimentwise Type I errors but with greater statistical power.  ZumaStat provides three such modifications, the Holm procedure, the Hochberg procedure and the False Discovery Rate procedure.  The Help menus in ZumaStat explain these methods and provide relevant citations.