Bayes' formula is a means to estimate disease probability based on the pres
ence of symptoms and the outcome of clinical tests. The probability helps t
o decide among competing diagnostic options. If, however, several diseases
present with similar symptoms, they may appear equally probable, and Bayes'
formula will fail as an aid to reach a diagnostic decision. The aim of thi
s study is to show how a merger of Bayes' principle with that of Ockham. ca
n help to decide in favour of one diagnosis among multiple, seemingly equal
ly probable diagnostic hypotheses. The hypotheses are compared to each othe
r with respect to those tests and symptoms which they fail to explain. The
unexplained tests and symptoms are used to estimate the probabilities for a
set of secondary diagnoses that match each one of the primary diagnoses. T
he more likely a secondary diagnosis appears, the less likely its correspon
ding primary diagnosis will remain as the sole diagnosis to explain all the
clinical findings. Even without a detailed calculation, the proposed conce
pt of using unexplained tests and symptoms to rate competing differential d
iagnoses could help the clinician to select the most probable diagnosis.