A growing body of evidence suggests that inconsistent hand preference is indicative of an increased disposition to update one's beliefs upon exposure to novel information. This is attributed to a facilitated exchange of information between the two brain hemispheres among inconsistent handers, compared to consistent handers. Currently available studies provide only indirect evidence for such an effect, were mostly based on small sample sizes, and did not provide measures of effect size. Small sample size is a major factor contributing to low replicability of research findings and false-positive results. We thus attempted to replicate Experiment 1 of Westfall, Corser and Jasper (2014), which appears to be representative of research on degree of handedness and belief updating in terms of the employed methods. We utilized data from a sample more than 10 times the size (N = 1243) of the original study and contrasted the commonly applied median-split technique to classify inconsistent and consistent handers with an empirically grounded classification scheme. Following a replication-extension approach, besides handedness, footedness was also explored. Only one out of 12 chi-squared tests reached significance and supported the original hypothesis that inconsistent handers stay with, or switch more often from, the status quo than consistent handers, depending on the valence of novel information. A small-telescopes analysis suggested that the original study had too low analytic power to detect its reported effect reliably. These results cast doubt on the assumption that inconsistent and consistent-handers differ in the tendency to update mental representations. We discuss the use of the median-split technique in handedness research, available neuroscientific evidence on interhemispheric interaction and inconsistent handedness, and venues of future research.

Although distributional inequality and concentration are important statistical concepts in many research fields (including economics, political and social science, information theory, and biology and ecology), they rarely are considered in psychological science. This practical primer familiarizes with the concepts of statistical inequality and concentration and presents an overview of more than a dozen useful, popular measures of inequality (including the Gini, Hoover, Rosenbluth, Herfindahl-Hirschman, Simpson, Shannon, generalized entropy, and Atkinson indices, and tail ratios). Additionally, an interactive web application (R Shiny) for calculating and visualizing these measures, with downloadable output, is described. This companion Shiny app provides brief introductory vignettes to this suite of measures, along with easy-to-understand user guidance. The Shiny app can readily be used as an intuitively accessible, interactive learning and demonstration environment for teaching and exploring these methods. We provide various examples for the application of measures of inequality and concentration in psychological science and discuss venues for further development.