CHI SQUARE TEST
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chi-Square Test The difference of proportions method is a very powerful method for estimat­ ing the effectiveness of campaigns and for other similar situations. However, there is another statistical test that can be used. This test, the chi-square test, is designed specifically for the situation when there are multiple tests and at least two discrete outcomes (such as response and non-response). 150 Chapter 5 The appeal of the chi-square test is that it readily adapts to multiple test groups and multiple outcomes, so long as the different groups are distinct from each other. This, in fact, is about the only important rule when using this test. As described in the next chapter on decision trees, the chi-square test is the basis for one of the earliest forms of decision trees.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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The place to start with chi-square is to lay data out in a table, This is a simple 2 × 2 table, which represents a test group and a control group in a test that has two outcomes, say response and nonresponse. shows the total values for each column and row; that is, the total number of responders and nonresponders (each column) and the total number in the test and control groups (each row). The response column is added for reference; it is not part of the calculation. What if the data were broken up between these groups in a completely unbi­ ased way? That is, what if there really were no differences between the columns and rows in the table? This is a completely reasonable question. We can calculate the expected values, assuming that the number of responders and non-responders is the same, and assuming that the sizes of the champion and challenger groups are the same. That is, we can calculate the expected value in each cell, given that the size of the rows and columns are the same as in the original data.