  We present a novel and easy-to-use method for calibrating error-rate based
confidence intervals to evidence-based support intervals. Support intervals are
obtained from inverting Bayes factors based on a parameter estimate and its
standard error. A $k$ support interval can be interpreted as "the observed data
are at least $k$ times more likely under the included parameter values than
under a specified alternative". Support intervals depend on the specification
of prior distributions for the parameter under the alternative, and we present
several types that allow different forms of external knowledge to be encoded.
We also show how prior specification can to some extent be avoided by
considering a class of prior distributions and then computing so-called minimum
support intervals which, for a given class of priors, have a one-to-one mapping
with confidence intervals. We also illustrate how the sample size of a future
study can be determined based on the concept of support. Finally, we show how
the bound for the type I error rate of Bayes factors leads to a bound for the
coverage of support intervals. An application to data from a clinical trial
illustrates how support intervals can lead to inferences that are both
intuitive and informative.
