PDF(Pubmed)
Abstract：
The field of neurotrauma is grappling with the effects of the recently identified replication crisis. As such, care must be taken to identify and perform the most appropriate statistical analyses. This will prevent misuse of research resources and ensure that conclusions are reasonable and within the scope of the data. We anticipate that Bayesian statistical methods will see increasing use in the coming years. Bayesian methods integrate prior beliefs (or prior data) into a statistical model to merge historical information and current experimental data. These methods may improve the ability to detect differences between experimental groups (i.e., statistical power) when used appropriately. However, researchers need to be aware of the strengths and limitations of such approaches if they are to implement or evaluate these analyses. Ultimately, an approach using Bayesian methodologies may have substantial benefits to statistical power, but caution needs to be taken when identifying and defining prior beliefs.
摘要：
神经创伤领域正在努力应对最近发现的复制危机的影响。因此，必须注意识别和执行最合适的统计分析。这将防止滥用研究资源，并确保结论合理且在数据范围内。我们预计，贝叶斯统计方法将在未来几年看到越来越多的使用。贝叶斯方法将先验信念（或先验数据）集成到统计模型中，以合并历史信息和当前实验数据。这些方法可以提高检测实验组之间差异的能力(即，统计能力)在适当使用时。然而,如果要实施或评估这些分析，研究人员需要意识到这些方法的优势和局限性。最终,使用贝叶斯方法的方法可能对统计能力有实质性的好处，但是在识别和定义先前的信念时需要谨慎。
