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。统计学意义不评估结果的可重复性。P值作为衡量重要性的指标主导了统计分析和决策，但令人惊讶的是，它几乎不能说明可复制性。观察到在典型实验的少量重复的一次模拟中，p值可以在很大的间隔内变化，这并不少见。从而说明p值是一个非常不可靠的量度。外部和内部可复制性分析都很重要。真正的外部复制研究在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、折叠法和自助法。最近，Copula函数作为一种适当的度量随机变量之间相关性的方法出现了，它为研究相关性提供了一种替代通常基于相关性的模型。

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.