Omitted variable bias occurs in most statistical models. Whenever a confounding variable that is correlated with both dependent and independent variables is omitted from a statistical model, estimated effects of included variables are likely to be biased due to omitted variables. This issue is particularly problematic in physics education research…

Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…

This paper examines sources of potential bias in systematic reviews and meta-analyses which can distort their findings, leading to problems with interpretation and application by practitioners and policymakers. It follows from an article that was published in the "Canadian Journal of Communication" in 1990, "Integrating Research…

Overlap-based measures are increasingly applied in the synthesis of single-subject research. This article considers two criticisms of overlap-based metrics, specifically that they do not measure magnitude of effect and do not adequately correspond with visual analysis. It is argued that these criticisms are based on fundamental misconceptions…

Problems inherent in relying solely on statistical significance testing as a means of data interpretation are reviewed. The biggest problem with statistical significance testing is that researchers have used the results of this testing to ascribe importance or meaning to their studies where such meaning often does not exist. Often researchers…

This paper examines the problems caused by relying solely on statistical significance tests to interpret results in contemporary social science. The place of significance testing in educational research has often been debated. Among the problems in reporting statistical significance are questions of definition and terminology. Problems are also…

This paper details the challenges encountered by authors summarizing evidence from a primary study to describe a treatment's effectiveness using an effect size (ES) estimate. Dilemmas that are encountered, including how to calculate and interpret the pertinent standardized mean difference ES for results from studies of various research designs,…

Problems with using stepwise analytic methods are discussed, and better alternatives are illustrated. To make the illustrations concrete, an actual data set, involving responses of 91 medical school admissions directors to 30 variables, was used. The 30 variables involved perceptions of barriers to medical school with respect to characteristics of…

Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing,…

This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…