Chapter 18: Misconceptions When people read, hear, or prepare research summaries, they sometimes have misconceptions about what is or isn't "sound practice" regarding the collection, analysis, and interpretation of data. Here are some of these common (and dangerous) misconceptions associated with the content of Chapter 18. If, at the end of the baseball season, the top 5 batters have these batting averages [.357, .351, .350, .350, and .348], their ranks would be 1, 2, 3, 3, 4. Nonparametric tests were invented specifically for situations where samples are nonrandom. Nonparametric tests have lower power than parametric tests. Since they are based on ranks, the results of nonparametric tests are easy to interpret. To compare 2 groups with a median test, it's necessary to compute each group's median. If the Wilcoxon matched-pairs signed-ranks test is used to compare a group's pretest and posttest performance, and if those people with identical pre and post scores are dropped from the analysis, this will cause the power of the Wilcoxon test to decrease. A Kruskal-Wallis one-way analysis of variance of ranks leads to an F-test that's usually presented in an ANOVA summary table. There are 2 independent variables in Friedman's two-way analysis of variance of ranks. Large-sample approximations of nonparametric tests should not be used unless the sample size(s) are equal to or greater than 30. Being "distribution-free," nonparametric tests have no underlying assumptions that deal with the distributional shape of population(s).