Responsible Use of Statistical Methods

Responsible Use of Statistical Methods focuses on good statistical practices. In the Introduction we distinguish between two types of activities; one, those involving the study design and protocol (a priori) and two, those actions taken with the results (post hoc.) We note that right practice is right ethics, the distinction between a mistake and misconduct and emphasize the importance of how the central hypothesis is stated. The Central Essay, Identification of Outliers in a Set of Precision Agriculture Experimental Data by Larry A. Nelson, Charles H. Proctor and Cavell Brownie, is a good paper to study. The Applied Ethics section focuses on objectivity and trustworthiness; we note that the misuse of statistics may be more widespread than misconduct. We have two Central Theme sections; 1) on setting up statistically rigorous hypothesis, and 2) on statistics and data management. The Case Study is courtesy of Case Western Reserve University, from their NSPE based case collection. For our Study Question, we present an ongoing argument concerning the United States census and good statistical practices, asking if statisticians should be involved in deciding how the census should be done. Our faculty guides for this module are Larry A. Nelson and Marcia Gumpertz, Department of Statistics. We would like to thank Cindy Levine of the NC State University Library for her article search assistance

ANOVA and rank tests when the number of treatments is large

In this paper we consider the analysis of variance (ANOVA) F-tests, and rank statistic analogs, for testing equality of treatment means in the one-way and two-way experimental layouts. The rank-based procedures include the Kruskal-Wallis and Friedman statistics with chi-squared critical values, and the "ANOVA on ranks" or F-versions of these procedures. We provide proofs of asymptotic normality for these statistics under the nonstandard assumption that the number of treatments converges to infinity while the number of replications per treatment remains finite. These results confirm the robustness of F-distribution critical values for nonnormal data in situations which have a large number of treatments.Kruskal-Wallis test Friedman test Central limit theorem Type I error robustness Nonnormality