  Several scientific fields including psychology are undergoing a replication
crisis. There are many reasons for this problem, one of which is a misuse of
p-values. There are several alternatives to p-values, and in this paper we
describe a complement that is geared towards replication. In particular, we
focus on confidence intervals for the probability that a parameter estimate
will exceed a specified value in an exact replication study. These intervals
convey uncertainty in a way that p-values and standard confidence intervals do
not, and can help researchers to draw sounder scientific conclusions. After
briefly reviewing background on p-values and a few alternatives, we describe
our approach and provide examples with simulated and real data. For linear
models, we also describe how confidence intervals for the exceedance
probability are related to p-values and confidence intervals for parameters.
