Complementing the P-value from null-hypothesis significance testing with a Bayes factor from null-hypothesis Bayesian testing
evidence and practice    

Complementing the P-value from null-hypothesis significance testing with a Bayes factor from null-hypothesis Bayesian testing

Helen Evelyn Malone Research assistant professor, School of Nursing and Midwifery, Trinity College Dublin, University of Dublin, Dublin, Ireland
Imelda Coyne Professor in children’s nursing and co-director, Research in Childhood Centre, School of Nursing and Midwifery, Trinity College Dublin, the University of Dublin, Dublin, Ireland

Background Classical frequentist statistics, including null-hypothesis significance testing (NHST), dominates nursing and medical research analysis. However, there is increasing recognition that null-hypothesis Bayesian testing (NHBT) merits inclusion in healthcare research analysis.

Aim To recommend that researchers complement the P-value from NHST with a Bayes factor from NHBT in their research analysis.

Discussion Reporting the P-value and a Bayes factor clarifies results that may be difficult to interpret using the P-value alone.

Conclusion NHBT offers statistical and practical advantages that complement NHST.

Nurse Researcher. 28, 4, 41-48. doi: 10.7748/nr.2020.e1756

Correspondence

hmalone@tcd.ie

Peer review

This article has been subject to external double-blind peer review and has been checked for plagiarism using automated software

Conflict of interest

None declared

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