It is known that under conditions of long-range dependence, and for ti
me series subordinated to Gaussian processes, the block bootstrap meth
od produces invalid estimators of the distribution of the sample mean
unless the limiting distribution is normal. In this paper we show that
the sampling window method produces valid, consistent estimators for
non-normal as well. as normal limits. Additionally, we introduce a met
hod for ''studentizing'' the sample mean of long-range dependent data,
and show that sampling window approximations of its distribution are
also valid. That result suggests that the sampling window method is us
eful for setting confidence intervals for a population mean in a parti
cularly wide range of circumstances. This conclusion is supported by a
small simulation study.