It is time to reform the quantitative methods courses in leadership programs -- typically, these are statistics courses with arcane statistics textbooks. There is growing evidence that these "rigorous" scientific methods actually mislead practice because the vast majority of practices found to be "effective" or…

We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers' decision making. Participants were presented with scenarios (testing a central hypothesis with p = 0.06 or p = 0.04) in a random order and surveyed about what they would do in each scenario. Participants showed significant…

This paper proposes an argument framework for the teaching of null hypothesis statistical testing and its application in support of research. Elements of the Toulmin (1958) model of argument are used to illustrate the use of p values and Type I and Type II error rates in support of claims about statistical parameters and subject matter research…

The sense that replicability is an important aspect of empirical science led Killeen (2005a) to define "p[subscript rep]," the probability that a replication will result in an outcome in the same direction as that found in a current experiment. Since then, several authors have praised and criticized 'p[subscript rep]," culminating…

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…

This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…

The concept of statistical significance is explained, with specific numerical illustrations. (MNS)

Surveyed AERA members regarding their perceptions of: statistical issues and statistical significance testing; the general linear model; stepwise methods; score reliability; type I and II errors; sample size; statistical probabilities as exclusive measures of effect size; p values as direct measures of result value; and p values evaluating…

An explanation of the misuse of statistical significance testing and the true meaning of "significance" is offered. Literature about the criticism of current practices of researchers and publications is reviewed in the context of tests of significance. The problem under consideration occurs when researchers attempt to do more than just establish…

The dangers of equating "statistical significance" with "real world" significance are summarized. When a finding is said to have "statistical significance," it means only that the same results would be likely to occur again if the study were repeated, not that the finding has any true personal or societal importance. (VW)

The study examining the validity of statistical conclusions of 49 early intervention studies found that 4 percent had adequate power to detect medium intervention effects and 18 percent to detect large intervention effects. Low statistical conclusion validity has practical consequences in program evaluation and cost-effectiveness determinations.…

Indicates that the explanation of what it means to obtain a significant F-ratio in discriminant function analysis, presented by K. E. Anderson in an earlier volume of Science Education, is incorrect. (JR)

Remarkability is introduced as a quantifiable attribute of given data and as a basis upon which one may rationally judge its scientific value. Applications of remarkability theory to various research and statistical problems and procedures are discussed. (DG)

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)