Consider a linear regression model with regression parameter beta and normally distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau = c^T beta - t where c and t are specified and a and c are linearly independent. Also suppose that we have uncertain prior information that tau = 0. Kabaila and Giri, 2009, JSPI, describe a new frequentist 1-alpha confidence interval for theta that utilizes this uncertain prior information. We compare this confidence interval with Bayesian 1-alpha equi-tailed and shortest credible intervals for theta that result from a prior density for tau that is a mixture of a rectangular "slab" and a Dirac delta function "spike", combined with noninformative prior densities for the other parameters of the model. We show that these frequentist and Bayesian interval estimators depend on the data in very different ways. We also consider some close variants of this prior distribution that lead to Bayesian and frequentist interval estimators with greater similarity. Nonetheless, as we show, substantial differences between these interval estimators remain.