title: Bayes without priors.
abstract: 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.
authors.affiliation: Center for Health Research, 3800 North Interstate Avenue, Portland, OR 97227, USA. mikel.aickin@kpchr.org
authors.forename: Mikel
authors.initials: M
authors.lastname: Aickin
mesh_terms: 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