A Likelihood-based Alternative to Null Hypothesis Significance Testing
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The logical and practical difficulties associated with research interpretation using P values and null hypothesis significance testing have been extensively documented. This paper describes an alternative, likelihood-based approach to P-value interpretation. The P-value and sample size of a research study are used to derive a likelihood function with a single parameter, the estimated population effect size, and the method of maximum likelihood estimation is used to calculate the most likely effect size. Comparison of the likelihood of the most likely effect size and the likelihood of the minimum clinically significant effect size using the likelihood ratio test yields the clinical significance support level (or S-value), a logical and easily understood metric of research evidence. This clinical significance likelihood approach has distinct advantages over null hypothesis significance testing. As motivating examples we demonstrate the calculation and interpretation of S-values applied to two recent widely publicised trials, WOMAN from the Lancet and RELIEF from the New England Journal of Medicine.
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