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The chi-square test dialog is shown below. Here, you enter the information that Maritz Stats needs to compute the results of a chi-square test.
Show me how to use it! To see an example of how to use the chi-square test, click here.
Show me how it works! For more information on the formula used for the chi-square test, click here.
Select the type of chi-square test that you want to perform. The goodness-of-fit test is used when you have a set of expected results and want to determine if the observed results are significantly different from the expected results. The Homogeneity or Independence test is a contingency problem dealing with the question of whether two variables in the same sample are independent.
The confidence level is 100% minus the probability (in percent form) of declaring a result significant when it is not. For most applications, the default of 95% should be used. However, you have the option of being more stringent (using the 99% confidence level) or less stringent (90% or 80% confidence level) based on your needs. The higher the confidence level, the less likely it is that differences will be found significant.
Set the number of rows and columns you need for the test. For a goodness-of-fit test, you are limited to entering one column of data for both Observed and Expected. Here, you simply set the number of rows that corresponds to the data you are entering. For a Homogeneity or Independence test, you can set both rows and columns, and the expected values are calculated for you.
Here you enter the data that you are testing. You should enter the counts rather than percentages. If you only know expected percentages, multiply them by the total observed count to get expected counts. You can navigate through the grid using Tab (to move one column to the right) or Enter (to move one column down).
The results are presented in three stages. First, the computed chi-square value is presented. This can be used to compare to a chi-square table if a significance level other than 80%, 90%, 95%, or 99% is desired. Second, the critical value for the test is presented based on the significance level selected. Finally, a simple "yes" or "no" indicates if the results are significant based on the information provided.