  We discuss the statistical analysis method for the worldvolume hybrid Monte
Carlo (WV-HMC) algorithm [arXiv:2012.08468], which was recently introduced to
substantially reduce the computational cost of the tempered Lefschetz thimble
method. In the WV-HMC algorithm, the configuration space is a continuous
accumulation (worldvolume) of deformed integration surfaces, and sample
averages are considered for various subregions in the worldvolume. We prove
that, if a sample in the worldvolume is generated as a Markov chain, then the
subsample in the subregion can also be regarded as a Markov chain. This ensures
the application of the standard statistical techniques to the WV-HMC algorithm.
We particularly investigate the autocorrelation times for the Markov chains in
various subregions, and find that there is a linear relation between the
probability to be in a subregion and the autocorrelation time for the
corresponding subsample. We numerically confirm this scaling law for a chiral
random matrix model.
