In this issue, the article entitled, Theory of Mind and Cognitive Function in Adults with Alstrom or Usher Syndrome, by Henricson, Frolander, Moller, and Lyxell reports the statistical results of Kruskal-Wallis tests using the statistic and Mann-Whitney tests using the statistic. These statistical tests are the nonparametric analog or match to parametric tests. The Kruskal-Wallis test is similar to the Analysis of Variance (or ANOVA) and the Mann-Whitney test is similar to the t-test. The reason why an experimenter uses Kruskal-Wallis and Mann-Whitney tests instead of ANOVA and t-tests is decided by what kind of data they are examining. To give a bit of background: A parametric test is a statistical test that is generally used when the data conforms to certain assumptions. One of the most important of these assumptions is that the data are distributed normally. If data are distributed normally, when the scores are plotted around the mean or average, they form a bell shape in which most of the scores are located around the mean and progressively fewer scores appear above or below the mean. If there are only two groups of data that are being compared to each other, and the data are normally distributed (as well as conforming with some other assumptions that I will not go into right now), a t-test can be used to compare those two means to each other to see if they are statistically significantly different. If there are three or more groups being compared to each other, then instead of a t-test, an ANOVA should be used. The t-test is really just a special case of the ANOVA in which the calculations are made easier because of the fact that only two groups are being compared. If the data are not normally distributed, the mathematics for the /-test and ANOVA often do not work out properly. A result can still be generated, but it might very well not truly reflect what is going on in the data. In this case, a statistical test that does not require the data to be normally distributed needs to be used. This test is the nonparametric alternative. If there are two groups being compared, a researcher can choose to use the Mann-Whitney test. It is much like the parametric /-test, but it is based on medians instead of means. Because of its basis in medians, the mathematics behind the Mann-Whitney test are different; the test also has the benefit of not requiring the data to be distributed along a bell curve. Similarly, if more than two groups are being compared and the data do not fit the assumptions for a parametric test, a Kruskal-Wallis test can be used, since it is also based on medians instead of means. What's with all the letters? a person may ask. The fact that the ANOVA gives an F statistic, the /-test a / statistic, the Mann-Whitney a U statistic, and the Kruskal-Wallis test an H statistic was essentially decided by the folks who developed these measures so that the test results could be talked about without having to repeat the tests' long names over and over. Treat the cryptic symbols simply as shorthand for referring to the test results. …