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To 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.
Bayesian t-test analysis tutorial.
A pedagogical framework was applied.
Simulated data generated in Microsoft Excel was uploaded to the Open Science Framework, accessible at: osf.io/4t9gn.
The 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 (H0) or the alternative (H1) hypotheses, better supports the experimental data and (2) the posterior probability distribution, with its median point estimate plus a 95% credible interval, quantifying the magnitude and uncertainty of the effect size estimate.
This 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.
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.
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.
The methodology aligns with van Doorn et al. (2021) guidelines for conducting and reporting a Bayesian analysis.
No patient or public contribution.
Background
The prevalence of short sleep duration is rising and is linked to chronic comorbidities, such as metabolic syndrome (MetS). Sleep extension interventions in adults with MetS comorbidities and short sleep duration are limited and vary widely in terms of approach and duration.
Objectives
This pilot study aimed to test the feasibility and acceptability of a personalized 12-week systematic sleep time extension intervention on post-intervention sleep outcomes in middle-aged adults at risk for MetS with actigraphy-estimated short sleep duration.
Methods
A single-arm, 12-week, 12-session systematic sleep time extension intervention was delivered weekly via videoconferencing. Feasibility and acceptability were assessed using retention rates and mean sleep diary completions. Sleep was estimated for 14 consecutive days prior to and immediately following the 12-week intervention using wrist actigraphy. Daytime sleepiness was assessed using the Epworth Sleepiness Scale. Paired sample t-tests modeled changes in study outcomes.
Results
Study participants (N = 41) had a mean age of 52 years and were mostly female and White; 86% attended >80% of sessions, and mean sleep diary completion was 6.7 diaries/week. Significant improvements in sleep from pre- to post-intervention included increased total sleep time, earlier sleep onsets, more regular sleep onsets, a higher sleep regularity index, and reduced daytime sleepiness. Extending sleep, as well as improving sleep timing and regularity in middle-aged adults with actigraphy-estimated short sleep duration and at risk for MetS, is feasible and acceptable.
Discussion
Behavioral sleep characteristics may be modifiable and present a novel behavioral paradigm for mitigating MetS risk. This pilot study provides a proof of concept for the feasibility, acceptability, and preliminary effectiveness of a systematic sleep time extension for middle-aged adults at risk for MetS with actigraphy-estimated short sleep duration. 
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