当前可复制性危机中的P值可变性评估
Assessment of P-value variability in the current replicability crisis
摘要：
近年来，数据的可获得性和计算工具的可获得性的增加为统计分析驱动的科学研究创造了前所未有的机会。统计的固有局限性限制了从数据中得出结论的可靠性，但统计方法的滥用日益令人关切。显著性、假设检验和相应的P值被认为是最广泛应用和滥用的做法。一种批评是，P值本质上不适合履行其作为科学假设可信度度量的表面角色。也有人认为，虽然P值可能有其作用，作为总结措施的影响，研究人员低估了随机性的程度P值。P值的高度变异性表明，在一项研究中获得了较小的P值，但在相似功效的重复研究中可能获得更大的P值。因此，“P值的可复制性”本身就值得怀疑。为了表征P值变异性，可以使用预测区间，其端点反映可能通过重复研究获得的P值的可能分布。不幸的是，目前使用的区间，P-区间，是基于不切实际的隐含假设。也就是说，P-区间的构建假设意味着在观察性研究中遇到大的效应量值的可能性很大，这会导致偏倚。作为P-区间的替代方案，我们开发了一种方法，通过为研究人员提供控制这些假设的方法，使他们具有灵活性。与P区间的端点不同，我们的区间的端点直接被解释为复制P值的概率界限，并且可以抵抗效应量分布的近似先验知识所带来的选择偏倚。
英文摘要：
Increased availability of data and accessibility of computational tools in recent years have created unprecedented opportunities for scientific research driven by statistical analysis. Inherent limitations of statistics impose constrains on reliability of conclusions drawn from data but misuse of statistical methods is a growing concern. Significance, hypothesis testing and the accompanying P-values are being scrutinized as representing most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of scientific hypothesis's credibility. It has also been suggested that while P-values may have their role as summary measures of effect, researchers underappreciate the degree of randomness in the P-value. High variability of P-values would suggest that having obtained a small P-value in one study, one is, nevertheless, likely to obtain a much larger P-value in a similarly powered replication study. Thus, "replicability of P-value" is itself questionable. To characterize P-value variability one can use prediction intervals whose endpoints reflect the likely spread of P-values that could have been obtained by a replication study. Unfortunately, the intervals currently in use, the P-intervals, are based on unrealistic implicit assumptions. Namely, P-intervals are constructed with the assumptions that imply substantial chances of encountering large values of effect size in an observational study, which leads to bias. As an alternative to P-intervals, we develop a method that gives researchers flexibility by providing them with the means to control these assumptions. Unlike endpoints of P-intervals, endpoints of our intervals are directly interpreted as probabilistic bounds for replication P-values and are resistant to selection bias contingent upon approximate prior knowledge of the effect size distribution.
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