  Bootstrap methods are increasingly accepted as one of the common approaches
in constructing confidence intervals in bibliometric studies. Typical bootstrap
methods assume that the statistical population is infinite. When the
statistical population is finite, a correction needs to be applied in computing
the estimated variance of the estimators and thus constructing confidence
intervals. We investigate the effect of overlooking the finiteness assumption
of the statistical population using a dataset containing all articles in Web of
Science (WoS) for Delft University of Technology from 2006 until 2009. We
regard the data as our finite statistical population and consider simple random
samples of various sizes. Standard bootstrap methods are firstly employed in
accounting for the variability of the estimates, as well as constructing the
confidence intervals. The results unveil two issues, namely that the
variability in the estimates does not decrease to zero as the sample size
approaches the population size and that the confidence intervals are not valid.
Both issues are addressed when accounting for a finite population correction in
the bootstrap methods.
