Nonparametric methods: The Wilcoxon rank-sum test (2)

Our Wilcoxon rank-sum test is the nonparametric equivalent of the independent t test. It actually calculates the sum of the ranks for both the IPA and the wheat beer group. As these beers are rated on a scale from 1 to 5, we regard a score of 5 as the highest possible score and a score of 1 as the lowest possible score. The largest possible difference score thus is 4.

Above are displayed the scores of our two groups. Each score is ranked. In case of a tie in rating, the average rank is taken. In R, you can do a Wilcoxon rank-sum test with the function wilcox.test(). You can give this function two different vectors with scores. Alternatively, you can use the formula interface when you are doing a rank-sum test. In this formula interface you specify your dependent variable ~ your independent variable, like so: wilcox.test(dependent_variable ~ independent_variable). If we would use this test on the data a provided in the table and we presume that this data is contained in a dataframe beer_data, we could do so in the following way: wilcox.test(beer_data$rating ~ beer_data$group). We can also specify the alternative parameter. By default this is set to "two.sided" meaning that we are doing a two-sided test. If you want to check whether you first group is greater than your second group, you change this to "greater".