- More widely used (and available in more software packages).
- Better when it does not make sense to combine the columns (e.g., where the columns represent different products being tested).
- More transparent, in that the tests compare numbers that are displayed on the table (whereas Cell Comparison involve computations that typically need to be computed using the raw data, except where the columns are mutually exclusive and exhaustive and the tests are simple).
- More intuitive to read (i.e., you can look at the tables and get a feeling for the meaning, without having to read and interpret the various letters).
- They provide equal emphasis to both 'high' and 'low' results (whereas with column comparisons you are drawn to the cells containing lots of letters and these are the ones which are highest).
- Superior statistical power. Each test involves the entire sample size, whereas the column comparisons only involve the sample in the two columns.
- Fewer false discoveries. When no multiple comparison corrections are used, column comparisons lead result in substantially more false discoveries than cell comparisons. And, when multiple comparisons are used, to protect against this column comparisons relative power drops even more. This is discussed in detail in Multiple Comparisons (Post Hoc Testing)
- Applicable to a wider number of types of data (i.e., can be conducted on any table, whereas column comparisons are perhaps only appropriate when the columns are mutually exclusive).