ArticleBioData mining2025
A simple guide to the use of Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics.
Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 41 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.
Evaluation of the Effectiveness of the Genomics & Science DOJO 3.0 Learning Method in Improving Research Skills and Scientific Writing Among Researchers in Indonesia: A Randomized Controlled Trial
Epigenetic Changes in Long COVID Patients: Unraveling the Gut-Immune Axis and Therapeutic Targets
Who cites it
41 citing papers in PubMed.
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- Mandibular Response in Class II Division 1 Growing Subjects: Clear Aligners vs. Rapid Maxillary Expansion Therapy. A Controlled Pilot Study.Orthodontics & craniofacial research · 2026Article
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- Decoding the allosteric grammar of protein kinases: A dual-stream framework integrating protein language models and energy landscape frustration analysis.Protein science : a publication of the Protein Society · 2026Article
- Dual-flow convolutional neural network for automatic measurement of left ventricular ejection fraction and global longitudinal strain in echocardiography.PLOS digital health · 2026Article
- Health related quality of life of patients with breast cancer in Malaysia: evidence from EQ-5D-5L and EORTC QLQ-C30.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.Briefings in bioinformatics · 2026Article
- Household soybean processing, protein content, and caregiver acceptability of complementary porridges in rural Malawi.BMC public health · 2026Article
- Implementation of an Integrated Pharmacology Curriculum in a Flipped, Small-Group, Case-Based, Pre-Clerkship Medical Curriculum Improved Student Performance.Pharmacology research & perspectives · 2026Article
- Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.Journal of chemical theory and computation · 2026Article
- MFSleepNet: An Interactive Multimodal Fusion Framework for Automatic Sleep Staging.Sensors (Basel, Switzerland) · 2026Article
- Study Protocol for the Development of an African Framework for Critical Care Nursing Based on Ubuntu Using Participatory Cooperative Inquiry: Decolonising Care.Nursing in critical care · 2026Article
- Psychosocial and Social Security Risks Linked to Vaccine Misinformation in Romania: Implications for Vaccination Acceptance and Public Policy.Behavioral sciences (Basel, Switzerland) · 2026Article
- Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.Molecular & cellular proteomics : MCP · 2026Article
- Evaluating the efficacy and readability of advanced large language models in responding to patients' frequently asked questions about chronic rhinosinusitis: a comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In an age when machine learning and artificial intelligence are broadly employed, traditional statistics can still provide insightful information and results quickly and at a low computational cost. Statistics, in fact, offers many useful tools to researchers, including a series of univariate statistical tests that can identify relationships between pairs of numeric samples: Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test. These tests generate several outcomes, including probability values (p-values) that can express a numerical quantity which accepts or rejects the null hypothesis, based on a certain threshold used. Although effective, these tests are often misused or employed in the wrong contexts, especially among biostatistics studies. Many scientific researchers do not seem to know how to choose one test over the others, and this misuse can lead to incorrect results and wrong conclusions. Here we present a simple theoretical and practical guide to the use of these four tests, first describing their theoretical properties and then displaying the results obtained by applying these tests to real-world medical datasets. Eventually, we explain when and how to use each test based on the data types of the samples considered. Our study can have a strong impact on scientific research by potentially influencing future studies involving these tests. Our recommendations, in turn, can help researchers produce more reliable and sound scientific results, thus increasing the quality of multiple scientific studies across various fields.
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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.