Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

Although it is assumed that increasing the institutionalization (or maturity) of project management in an organization leads to greater project success, the literature has diverse views. The purpose of this mixed methods study was to examine the correlation between project management maturity and IT/IS project outcomes. The sample consisted of two…

Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian…

This study aims to understand how EFL learners in different reading proficiency levels comprehend L2 texts, using five-component skills involving measures of (1) vocabulary knowledge, (2) drawing inferences and predictions, (3) knowledge of text structure and discourse organization, (4) identifying the main idea and summarizing skills, and (5)…

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…