Avoiding negative reviewer comments: common statistical errors in anesthesia journals.
10.4097/kjae.2016.69.3.219
- Author:
Sangseok LEE
1
Author Information
1. Department of Anesthesiology and Pain Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea. s2248@paik.ac.kr
- Publication Type:Review
- Keywords:
Biomedical research;
Research;
Statistical analysis;
Statistical errors;
Statistics
- MeSH:
Anesthesia*;
Methods
- From:Korean Journal of Anesthesiology
2016;69(3):219-226
- CountryRepublic of Korea
- Language:English
-
Abstract:
Manuscripts submitted to journals should be understandable even to those who are not experts in a particular field. Moreover, they should use publicly available materials and the results should be verifiable and reproducible. Readers and reviewers will want to check the strengths and weaknesses of the research study design, and ways to make this determination should be clear through proper analysis methods. Studies should be described in detail so as to help readers understand the results. Statistical analysis is one of the key methods by which to do this. The inappropriate application of statistical methods could be misleading to readers and clinicians. While many researchers describe their general research methods in detail, statistical methods tend to be described briefly, with certain omissions or errors or other incorrect aspects. For instance, researchers should describe whether the median or mean was used, whether parametric or nonparametric tests were used, whether the data meet the normality test, whether confounding factors were corrected, and whether stratification or matching methods were used. Statistical analysis regardless of the program should be reported correctly. The results may be less reliable if the statistical assumptions before applying the statistical method are not met. These common errors in statistical methods originate from the researcher's lack of knowledge of statistics and/or from the lack of any statistical consultation. The aim of this work is to help researchers know what is important statistically and how to present it in papers.
