Significance measure and result replicability in prediction modelling

结果的可复制性被认为是科学研究方法论的基础。统计学显著性不评价结果的可重复性。 p值作为衡量显著性的指标主导着统计分析和决策制定，但令人惊讶的是，它几乎没有告诉我们可复制性。在典型实验的少量重复的一次模拟中，观察到p值可以在很大的间隔内变化并不罕见。这说明p值是一个非常不可靠的指标。外部和内部的可复制性分析都很重要。真正的外部复制研究很多时候是不切实际的，因此研究人员转向内部方法，如交叉验证，刀切法和自助法。最近copula函数已经出现作为一个适当的措施之间的依赖性随机变量，并提供了一个替代模型，通常实践的相关性为基础的模型来研究的依赖性。

Result replicability is considered fundamental in scientific research methodology. Statistical significance does not evaluate result replicability. The p-value as a measure of significance dominates statistical analysis and decision making however it surprisingly tells little about replicability. It is not uncommon to observe that in one simulation of small number of repeats of a typical experiment, p-value can vary over a large interval. Thus illustrating that p-value is a very unreliable measure. Both external and internal replicability analyses are important. True external replication studies are many times impractical, and therefore researchers turn to internal methods like cross-validation, the jackknife and the bootstrap. Recently copula functions have emerged as an appropriate measure of dependence among random variables and provide an alternative model to the commonly practised correlation based models for studying the dependence.