Null hypothesis testing as a tool in research is defended. Six examples are offered of situations in which, if all the researcher could do was "reject H₀ at α = .05" the scientific contribution would still be substantial. The examples are drawn from …
The Publication Manual of the American Psychological Association (American Psychological Association, 2001, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the …
Null hypothesis significance testing (NHST) is arguably the most widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is …
In recent years, researchers have attempted to provide an indication of the prevalence of inflated Type 1 error rates by analyzing the distribution of p-values in the published literature. De Winter & Dodou (2015) analyzed the distribution (and its …
Monte-Carlo simulation was used to model the biasing of effect sizes in published studies. The findings from the simulation indicate that, when a predominant bias to publish studies with statistically significant results is coupled with inadequate …
This study compiles information from more than 250 meta-analyses conducted over the past 30 years to assess the magnitude of reported effect sizes in the organizational behavior (OB)/human resources (HR) literatures. Our analysis revealed an average …
Sample correlations converge to the population value with increasing sample size, but the estimates are often inaccurate in small samples. In this report we use Monte-Carlo simulations to determine the critical sample size from which on the magnitude …
A central goal of translational neuroimaging is to establish robust links between brain measures and clinical outcomes. Success hinges on the development of brain biomarkers with large effect sizes. With large enough effects, a measure may be …
Individual differences researchers very commonly report Pearson correlations between their variables of interest. Cohen (1988) provided guidelines for the purposes of interpreting the magnitude of a correlation, as well as estimating power. …
Traditional methods of analyzing data from psychological experiments are based on the assumption that there is a single random factor (normally participants) to which generalization is sought. However, many studies involve at least two random factors …