Hu Liang-ping, Liu Hui-gang
Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China.
Zhonghua Yi Xue Za Zhi. 2005 Jul 20;85(27):1936-40.
To point out the crux of why so many people failed to grasp statistics and to bring forth a "triple-type theory of statistics" to solve the problem in a creative way.
Based on the experience in long-time teaching and research in statistics, the "three-type theory" was raised and clarified. Examples were provided to demonstrate that the 3 types, i.e., expressive type, prototype and the standardized type are the essentials for people to apply statistics rationally both in theory and practice, and moreover, it is demonstrated by some instances that the "three types" are correlated with each other. It can help people to see the essence by interpreting and analyzing the problems of experimental designs and statistical analyses in medical research work.
Investigations reveal that for some questions, the three types are mutually identical; for some questions, the prototype is their standardized type; however, for some others, the three types are distinct from each other. It has been shown that in some multifactor experimental researches, it leads to the nonexistence of the standardized type corresponding to the prototype at all, because some researchers have committed the mistake of "incomplete control" in setting experimental groups. This is a problem which should be solved by the concept and method of "division".
Once the "triple-type" for each question is clarified, a proper experimental design and statistical method can be carried out easily. "Triple-type theory of statistics" can help people to avoid committing statistical mistakes or at least to decrease the misuse rate dramatically and improve the quality, level and speed of biomedical research during the process of applying statistics. It can also help people to improve the quality of statistical textbooks and the teaching effect of statistics and it has demonstrated how to advance biomedical statistics.
指出众多人难以掌握统计学的症结所在,并创造性地提出“统计学三型理论”以解决该问题。
基于长期统计学教学与研究经验,提出并阐明“三型理论”。通过实例说明表达型、原型和标准型这三种类型是人们在理论和实践中合理应用统计学的关键要素,且通过实例证明“三型”相互关联。通过解读和分析医学研究工作中的实验设计与统计分析问题,帮助人们洞察本质。
调查发现,对于某些问题,三型相互等同;对于某些问题,原型即其标准型;然而,对于另一些问题,三型彼此不同。结果表明,在一些多因素实验研究中,由于部分研究者在设置实验组时犯了“控制不全”的错误,导致根本不存在与原型相对应的标准型。这是一个需借助“划分”概念和方法来解决的问题。
一旦明确每个问题的“三型”,就能轻松进行恰当的实验设计和统计方法选择。“统计学三型理论”可帮助人们避免犯统计错误,或至少大幅降低误用率,提高生物医学研究在应用统计学过程中的质量、水平和速度。它还能帮助人们提高统计学教材质量和统计学教学效果,论证了推进生物医学统计学的方法。