ArticleScientific reports2021
Use of the p-values as a size-dependent function to address practical differences when analyzing large datasets.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Unbiased distance correlation with sample-size-aware confidence bounds for comparative omics network analysis.Frontiers in bioinformatics · 2026Article
- Selecting the optimal cell migration assay: fundamentals and practical guidelines.Nature methods · 2026Review
- Boxcar Imaging FCS Reveals Membrane Raft Stabilization Kinetics in Antigen-Stimulated Mast Cells.bioRxiv : the preprint server for biology · 2025Article
- PhotoFiTT: a quantitative framework for assessing phototoxicity in live-cell microscopy experiments.Nature communications · 2025Article
- Critical roles of plasma membrane dynamics and heterogeneity at the early stage of antigen-stimulated FcεRI signaling in mast cells.Biochemical Society transactions · 2025Review
- Ultra-processed food intake and brain health in middle-aged and older adults.The journal of nutrition, health & aging · 2025Article
- Author's response to letter to editor.Clinical rheumatology · 2025Article
- The Role of Statistical Power: A Study of Relationship Between Emotional and Conduct Problems, Sociodemographic Factors, and Smoking Behaviours in Large and Small Samples of Latvian Adolescents.Medicina (Kaunas, Lithuania) · 2025Article
- Practical considerations for data exploration in quantitative cell biology.Journal of cell science · 2025Article
- Multicenter study on the distribution and prevalence of human papillomavirus types in Hangzhou, Zhejiang from 2017 to 2023.Scientific reports · 2025Article
- Comparing ECG Lead Subsets for Heart Arrhythmia/ECG Pattern Classification: Convolutional Neural Networks and Random Forest.CJC open · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision-making. In the largely extended null hypothesis significance testing for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS .
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