Students often believe that statistical significance is the only determinant of whether a quantitative result is "important." In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction,…

In their response to our paper, Nicholson and Ridgway agree with the majority of what we wrote. They echo our concerns about the misuse of inferential statistics and NHST in particular. Very little of their response explicitly challenges the points we made but where it does their defence of the use of inferential techniques does not stand up to…

Many librarians use data from surveys to make decisions about how to spend money or allocate staff, often making use of popular online tools like Survey Monkey. In this era of reduced budgets, low staffing, stiff competition for new resources, and increasingly complex choices, it is especially important that librarians know how to get strong,…

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…

This article discusses issues associated with statistical testing conducted with data from clustered school samples. Empirical researchers often conduct tests of statistical inference on sample data to ascertain the extent to which differences exist within groups in the population. Typically, much school-related data are collected from students.…

Very little explanatory power is required in order for regressions to exhibit statistical significance. This article discusses some of the causes and implications. (Contains 2 tables.)

This paper offers a practical guide to use null hypotheses significance testing and its alternatives. The focus is on improving the quality of statistical inference in quantitative communication research. More consistent reporting of descriptive statistics, estimates of effect size, confidence intervals around effect sizes, and increasing the…

Addresses the state of the art in use of statistical significance tests and effect size interpretation, explicating the current debate regarding hypothesis testing; reviewing the newly published American Psychological Association Task Force on Statistical Inference report on statistical inference; examining current trends in reporting practices in…

Researchers, journal editors, textbook authors, and those responsible for writing publication manuals must work together to enhance the thoughtful reporting of statistical results and to make clear the necessity for reporting effect sizes. (SLD)

Suggests that researchers should report the results of appropriate significance tests and the effect sizes associated with each test. Discusses the roles of textbook authors, publication manuals, and journal editors in leading the movement to better statistical reporting. (SLD)

Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…