title: [Triple-type theory of statistics and its application in the scientific research of biomedicine].
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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".
  
  
  CONCLUSIONS
  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.
authors.affiliation: Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China.
authors.forename: Liang-ping
authors.initials: LP
authors.lastname: Hu
authors.forename: Hui-gang
authors.initials: HG
authors.lastname: Liu
mesh_terms: D035843:Biomedical Research; D013223:Statistics as Topic / Q000145:classification* / Q000379:methods*