• To be prepared as Bayesian statistical analysis is likely to become more commonly applied in nursing research
• To consider how a Bayesian analysis complements null-hypothesis significance testing and adds value to research results
• To explore simple ways to carry out a Bayesian analysis and interpret a Bayes factor result
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. doi: 10.7748/nr.2020.e1756Peer review
This article has been subject to external double-blind peer review and has been checked for plagiarism using automated softwareCorrespondence
Malone H, Coyne I (2020) Complementing the P-value from null-hypothesis significance testing with a Bayes factor from null-hypothesis Bayesian testing. Nurse Researcher. doi: 10.7748/nr.2020.e1756
Published online: 05 November 2020
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