ArticleThe Journal of cell biology2025
Alternative to the statistical mass confusion of testing for "no effect".
Article in The Journal of cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrins and tetraspanins mediate CD36-actin coupling and regulate CD36 organization and signaling.The Journal of cell biology · 2026Article
- Effect sizes for nonparametric tests.Biochemia medica · 2026Review
- Proteomic signals are not equal clinical phenotypes: redefining evidence standards in arbovirus-SARS-CoV-2 cross-reactivity.Frontiers in immunology · 2026Review
Corrections and comments
- Update of
Authors and funding
1 author.
Funding
Abstract
In cell biology, statistical analysis means testing the hypothesis that there was no effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The solution is for analysis of measurements to start and end with an interpretation of effect size. In this manuscript, I walk through some of the common critiques of significance testing and how they relate to experimental cell biology. I argue that careful consideration of effect size should be returned to its central position in the planning and discussion of cell biological research. To facilitate this shift in focus, I recommend replacing P values with confidence intervals as cell biology's default statistical analysis.
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.