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A Tutorial on Conducting and Interpreting a Bayesian Independent T-Test Using Open-Source Software.

Helen Evelyn Malone1, Imelda Coyne1

  • 1School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland.

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Summary
This summary is machine-generated.

This tutorial demonstrates a Bayesian independent t-test using open-source software for nursing research. It offers advantages over frequentist methods, improving decision-making and reducing bias.

Keywords:
Bayes factorBayesian independent t‐testBayesian inferenceBayesian parameter estimationcredibility intervalfrequentist independent t‐testmidwivesnursesp valuetutorial

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Area of Science:

  • Nursing and Midwifery Research
  • Biostatistics
  • Quantitative Research Methods

Background:

  • Traditional nursing and midwifery research heavily relies on frequentist statistical techniques like p-values and confidence intervals.
  • Bayesian statistical methods offer an alternative approach with potential advantages for data analysis and interpretation.

Purpose of the Study:

  • To provide a practical, worked-out example of a Bayesian independent t-test using simulated data and open-source software.
  • To highlight the statistical principles and literature supporting the use of Bayesian methods as a complement or replacement for frequentist t-tests.

Main Methods:

  • A pedagogical framework was applied to a Bayesian independent t-test tutorial.
  • Simulated data from a hypothetical nurse education intervention in a randomized controlled trial design was used.
  • Analysis was conducted using open-source software (JASP), with data uploaded to the Open Science Framework.

Main Results:

  • The Bayesian independent t-test in JASP yields a Bayes factor to quantify evidence supporting the null (H0) or alternative (H1) hypotheses.
  • It also provides a posterior probability distribution, including a median point estimate and a 95% credible interval for the effect size.

Conclusions:

  • Bayesian analysis offers practical, statistical, and ethical advantages for nursing and midwifery research, including sequential analysis and optimal stopping rules.
  • Adoption of Bayesian methods can enhance research efficiency, improve decision-making with probabilistic evidence, and mitigate publication bias by avoiding binary interpretations.