ArticleJournal of advanced nursing2026
A Tutorial on Conducting and Interpreting a Bayesian Independent T-Test Using Open-Source Software.
Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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Who cites it
2 citing papers in PubMed.
- Learning to Learn: Initial Treatment of a Partially Acquired Grammatical Form Speeds Later Learning of Rarely Used Forms for Children With Developmental Language Disorder.Language, speech, and hearing services in schools · 2026Article
- Semantic knowledge of words is necessary to produce an incidental self-reference effect.Psychonomic bulletin & review · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo demonstrate a worked-out example of a Bayesian independent t-test using open-source software, simulated data, a hypothetical nurse education intervention and a randomised controlled study design. This tutorial explains relevant Bayesian concepts and highlights literature that provides statistically principled justifications for replacing or complementing the frequentist independent t-test with its Bayesian counterpart.
designBayesian t-test analysis tutorial.
methodsA pedagogical framework was applied. DATA SOURCE: Simulated data generated in Microsoft Excel was uploaded to the Open Science Framework, accessible at: osf.io/4t9gn.
resultsThe Bayesian independent t-test in JASP provides: (1) a Bayes factor quantifying the relative evidence for determining which of two competing theories, that is, the null (H
conclusionsThis article provides a practical method for nursing and midwifery researchers to conduct Bayesian analysis, offering statistical, practical and ethical advantages, including the application of sequential analysis and optimal stopping rules enhancing research efficiency. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: This article increases awareness of the feasibility and benefits of Bayesian analysis in nursing and midwifery research, emphasising its ease of implementation through open-source software. Clear step-by-step guidance is provided to support its wider adoption and strengthen methodological rigour in nursing and midwifery research. IMPACT: Nursing and midwifery research has traditionally relied upon frequentist statistical techniques, based on p values and confidence intervals. Bayesian methods can: (a) improve nursing and midwifery decision-making with probabilistic evidence and (b) reduce publication bias by avoiding binary interpretation of research results. REPORTING
methodThe methodology aligns with van Doorn et al. (2021) guidelines for conducting and reporting a Bayesian analysis. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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.