ReviewKorean journal of anesthesiology2016
Nonparametric statistical tests for the continuous data: the basic concept and the practical use.
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 113 papers.
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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.
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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.
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Who cites it
113 citing papers in PubMed.
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- Contrasting Patterns in Ambient PMGeoHealth · 2026Article
- The Coral Host Microbiome Modulates the Virulence of the Bacterial Pathogen Vibrio coralliilyticus.Environmental microbiology · 2026Article
- Wearable Monitoring Captures Sleep Disturbances in Patients With Chronic Inflammatory Demyelinating Polyneuropathy.Journal of the peripheral nervous system : JPNS · 2025Observational
- Statistical consideration in nephrology research.Kidney research and clinical practice · 2025Article
- Cumulative Radiation Exposure Post Aneurysmal Subarachnoid Haemorrhage.Clinical neuroradiology · 2025Observational
- Decline in Serum Lysophosphatidylcholine Species in Patients with Severe Inflammatory Bowel Disease.Journal of clinical medicine · 2025Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
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