ReviewKorean journal of anesthesiology2016
Avoiding negative reviewer comments: common statistical errors in anesthesia journals.
Review in Korean journal of anesthesiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Article
- Use of appropriate statistical tools in biomedical research: Current trend & status.The Indian journal of medical research · 2023Article
- Minimising statistical errors in the research domain: Time to work harder and dig deeper!Indian journal of anaesthesia · 2021Article
- Statistical Accuracy in Rheumatology Research.Mediterranean journal of rheumatology · 2019Review
- What is the proper way to apply the multiple comparison test?Korean journal of anesthesiology · 2018Article
- Understanding one-way ANOVA using conceptual figures.Korean journal of anesthesiology · 2017Review
- Alternatives to P value: confidence interval and effect size.Korean journal of anesthesiology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.