Bayes without priors.
OBJECTIVE
Classical statistical inference has attained a dominant position in the expression and interpretation of empirical results in biomedicine. Although there have been critics of the methods of hypothesis testing, significance testing (P-values), and confidence intervals, these methods are used to the exclusion of all others.


METHODS
An alternative metaphor and inferential computation based on credibility is offered here.


RESULTS
It is illustrated in three datasets involving incidence rates, and its advantages over both classical frequentist inference and Bayesian inference, are detailed.


CONCLUSIONS
The message is that for those who are unsatisfied with classical methods but cannot make the transition to Bayesianism, there is an alternative path.
Aickin
Mikel
M
Center for Health Research, 3800 North Interstate Avenue, Portland, OR 97227, USA. mikel.aickin@kpchr.org
D001130:Arizona / Q000453:epidemiology; D001499:Bayes Theorem; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006801:Humans; D007087:Illinois / Q000453:epidemiology; D015994:Incidence; D008654:Mesothelioma / Q000401:mortality; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000453:epidemiology; D013223:Statistics as Topic; D015996:Survival Rate