- Helen Evelyn Malone: School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland.
- Imelda Coyne: School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland.
BACKGROUND: The statistical shortcomings of null hypothesis significance testing (NHST) are well documented, yet it continues to be the default paradigm in quantitative healthcare research. This is due partly to unfamiliarity with Bayesian statistics.
AIM: To highlight some of the theoretical and practical benefits of using Bayesian analysis.
DISCUSSION: A growing body of literature demonstrates that Bayesian analysis offers statistical and practical benefits that are unavailable to researchers who rely solely on NHST. Bayesian analysis uses prior information in the inference process. It tests a hypothesis and yields the probability of that hypothesis, conditional on the observed data; in contrast, NHST checks observed data - and more extreme unobserved data - against a hypothesis and yields the long-term probability of the data based on repeated hypothetical experiments. Bayesian analysis provides quantification of the evidence for the null and alternative hypothesis, whereas NHST does not provide evidence for the null hypothesis. Bayesian analysis allows for multiplicity of testing without corrections, whereas NHST multiplicity requires corrections. Finally, it allows sequential data collection with variable stopping, whereas NHST sequential designs require specialised statistical approaches.
CONCLUSION: The Bayesian approach provides statistical, practical and ethical advantages over NHST.
IMPLICATIONS FOR PRACTICE: The quantification of uncertainty provided by Bayesian analysis - particularly Bayesian parameter estimation - should better inform evidence-based clinical decision-making. Bayesian analysis provides researchers with the freedom to analyse data in real time with optimal stopping when the data is persuasive and continuing when data is weak, thereby ensuring better use of the researcher's time and resources.