ReviewKorean journal of anesthesiology2017
Understanding one-way ANOVA using conceptual figures.
Review in Korean journal of anesthesiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- More rigorously and more transparently: recommendations for reporting statistical methods.The Korean journal of pain · 2026Article
- Nuclear DNA Content Analyses by Flow Cytometry of Saffron (Current issues in molecular biology · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
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- Measuring attitudes toward artificial intelligence: validation of the Chinese ATTARI-12 and invariance across genders and academic disciplines.Frontiers in psychology · 2026Article
- Topological resilience of EEG-based functional networks during virtual reality-induced emotional states.Frontiers in human neuroscience · 2026Article
- Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.PloS one · 2026Article
- Visual attention and subjective preference in mascot design: evidence from eye-tracking, ratings, and fsQCA.Frontiers in psychology · 2026Article
- Exploring the effectiveness and awareness of Anki flashcard application on medical student's academic performance: a cross-sectional study from Saudi Arabia.Frontiers in medicine · 2026Article
- Growth strategy of aerial green algae on building materials in the temperate climate zone and its relevance to substrate biodeterioration.Scientific reports · 2025Article
56 more citing papers are in PubMed but not listed here.
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
Analysis of variance (ANOVA) is one of the most frequently used statistical methods in medical research. The need for ANOVA arises from the error of alpha level inflation, which increases Type 1 error probability (false positive) and is caused by multiple comparisons. ANOVA uses the statistic F, which is the ratio of between and within group variances. The main interest of analysis is focused on the differences of group means; however, ANOVA focuses on the difference of variances. The illustrated figures would serve as a suitable guide to understand how ANOVA determines the mean difference problems by using between and within group variance differences.
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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.