Malone Helen Evelyn, Coyne Imelda
School of Nursing and Midwifery, Trinity College Dublin, University of Dublin, Dublin, Ireland.
School of Nursing and Midwifery, Trinity College Dublin, the University of Dublin, Dublin, Ireland.
Nurse Res. 2020 Nov 4. doi: 10.7748/nr.2020.e1756.
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.
To recommend that researchers complement the P-value from NHST with a Bayes factor from NHBT in their research analysis.
Reporting the P-value and a Bayes factor clarifies results that may be difficult to interpret using the P-value alone.
NHBT offers statistical and practical advantages that complement NHST.
包括零假设显著性检验（NHST）在内的经典频率统计学在护理和医学研究分析中占据主导地位。然而，越来越多的人认识到，零假设贝叶斯检验（NHBT）在医疗保健研究分析中值得采用。
建议研究人员在其研究分析中用NHBT的贝叶斯因子补充NHST的P值。
报告P值和贝叶斯因子可澄清仅使用P值可能难以解释的结果。
NHBT提供了补充NHST的统计和实际优势。
