  Quasi-Monte Carlo (QMC) points are a substitute for plain Monte Carlo (MC)
points that greatly improve integration accuracy under mild assumptions on the
problem. Because QMC can give errors that are $o(1/n)$ as $n\to\infty$,
changing even one point can change the estimate by an amount much larger than
the error would have been and worsen the convergence rate. As a result, certain
practices that fit quite naturally and intuitively with MC points are very
detrimental to QMC performance. These include thinning, burn-in, and taking
sample sizes such as powers of $10$, other than the ones for which the QMC
points were designed. This article looks at the effects of a common practice in
which one skips the first point of a Sobol' sequence. The retained points
ordinarily fail to be a digital net and when scrambling is applied, skipping
over the first point can increase the numerical error by a factor proportional
to $\sqrt{n}$ where $n$ is the number of function evaluations used.
