Hand and Eye Dominance in Sport: Are Cricket Batters Taught to Bat Back-to-Front?

Background: When first learning to bimanually use a tool to hit a target (e.g., when chopping wood or hitting a golf ball), most people assume a stance that is dictated by their dominant hand. By convention, this means that a ‘right-handed’ or ‘left-handed’ stance that places the dominant hand closer to the striking end of the tool is adopted in many sports. Objective: The aim of this study was to investigate whether the conventional stance used for bimanual hitting provides the best chance of developing expertise in that task. Methods: Our study included 43 professional (international/first-class) and 93 inexperienced (<5 years’ experience) cricket batsmen. We determined their batting stance (plus hand and eye dominance) to compare the proportion of batters who adopted a reversed stance when batting (that is, the opposite stance to that expected based on their handedness). Results: We found that cricket batsmen who adopted a reversed stance had a stunning advantage, with professional batsmen 7.1 times more likely to adopt a reversed stance than inexperienced batsmen, independent of whether they batted right or left handed or the position of their dominant eye. Conclusion: Findings imply that batsmen who adopt a conventional stance may inadvertently be batting ‘back-to-front’ and have a significant disadvantage in the game. Moreover, the results may generalize more widely, bringing into question the way in which other bimanual sporting actions are taught and performed

Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis

The effect sizes of studies included in a meta‐analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so‐called between‐study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between‐study variance facilitates interpretation of the meta‐analytic results. Two methods that are recommended to be used for creating such a confidence interval are the Q‐profile and generalized Q‐statistic method that both make use of the Q‐statistic. These methods are exact if the assumptions underlying the random‐effects model hold, but these assumptions are usually violated in practice such that confidence intervals of the methods are approximate rather than exact confidence intervals. We illustrate by means of two Monte‐Carlo simulation studies with odds ratio as effect size measure that coverage probabilities of both methods can be substantially below the nominal coverage rate in situations that are representative for meta‐analyses in practice. We also show that these too low coverage probabilities are caused by violations of the assumptions of the random‐effects model (ie, normal sampling distributions of the effect size measure and known sampling variances) and are especially prevalent if the sample sizes in the primary studies are small