Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…

Comments on a paper by H. S. Kim, D. K. Sherman, and S. E. Taylor in regards to the consideration of culture, ethnicity, and national origin in the study of individual and group differences and social interactions. Erard claims that their attempt to infer from small, mostly college-student samples examined on a handful of variables in a few dozen…

Responds to Biskin's comments (this issue) on the significance test controversy. Highlights areas of agreement (importance of replication evidence, importance of effect sizes) and disagreement (influence of sample size, evaluation of populations vs. samples, significance of Carver's article). Includes further recommendations for reporting research…

Makes the case that science is best served when researchers focus on the size of effects and their practical significance. Advocates the use of confidence intervals for deciding whether chance or sampling variability is an unlikely explanation for an observed effect. Calls for more emphasis on effect sizes in the next edition of the American…

This commentary on EC 603 695 argues that significance testing is a necessary but insufficient condition for positivistic research, that judgment-based assessment and single-subject research are not substitutes for significance testing, and that sampling fluctuation should be considered as one of numerous epistemological concerns in any…

This paper argues that judgment-based assessment of data from multiply replicated single-subject or small-N studies should replace normative-based (p=less than 0.05) assessment of large-N research in the clinical sciences, and asserts that inferential statistics should be abandoned as a method of evaluating clinical research data. (Author/JDD)

Description of common errors found in the statistical methodologies of research carried out by librarians, focuses on sampling and generalizability. The discussion covers the need to either adapt library research to the statistical abilities of librarians or to educate librarians in the proper use of statistics. (15 references) (CLB)

In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…