The flipside is that you can give off the impression of having a large user base/product catalog/etc if you number things sequentially... but start at a large non-round number.
From the article:
If starting with an initial gap between 0 and the lowest
sample (sample minimum), the average gap between samples is
(m - k)/k; the -k being because the samples themselves are
not counted in computing the gap between samples.
On the other hand, if instead of ordering them sequentually I roll a die and add the number of spots to the previous serial number, I think I can trick you into thinking I have three times as many tanks as I actually do. In fact, I feel quite confident of it.
For example, having seen 250 IDs in the 1…1000 range and 200 in the 1001…2000 range, the next ID in the 1…2000 range I see should fall in the 1…1000 range with probability 750/(750 + 800) ~= 0.48 in the 'normal' case, and around 36/(36 + 86) ~= 0.30 with your method of doling out IDs.
And I think it would be a factor of 3.5 (the expected number of eyes on a throw of a die). That's why I expect your method to dole out 286 out of every 1000 IDs.
But it would require me from checking for this, depend on finding such discrepancies in samples, and increase the variance on my estimates for a given sample size.
So there are ways to game the statistical analysis.