Are you completely sure about that?
I suppose many readers of this thread are more knowledgeable about statistics than I am. I would appreciate hearing from the knowledgeable readers whether or not variance in the observed values makes a difference in the cases discussed in the submitted article.
As the above triva factoid points out, the standard deviation is an important summary statistic. More interestingly by using mean, variance (or sd), skew, and kurtosis, you can describe almost any centrally concentrated distribution. Even distribution with heavy tails.
I think what the OP meant is that most 3+ sigma results are not truly 3+ sigma, because most distributions in this world are not gaussian, but instead have large wings. SD is most useful when you know what the underlying distribution is. Currently it's more in fashion to communicate spread using confidence intervals because they presume less about the underlying distribution.
I should have said something more like "the standard deviation calculated from a sample set is only generally applicable in so far as one is willing to make assumptions that the sample set is representative of the distribution as a whole". The default assumption in traditional statistics (such as quoting p-values) is that the distribution is normal, and in real world situations often not the case.
Your restatement is right on, although I'd go farther and say that standard deviations (and confidence intervals) are only useful metrics with regard to the particular assumptions one is willing to make about underlying distribution. Yes, you can calculate these measures, but they won't help you if your assumptions are irreparably flawed.
You could quibble about my exact phrasing, but yes, I'm completely sure about that. This is the 'black swan' problem writ small. I don't mean that a high standard deviation should be ignored for real-world distributions, but I do mean that a low standard deviation carries very little weight unless a normal distribution is presumed.
I'm hard pressed to relate this to the cases discussed in the article, as those cases are shy on detail, but the DB2 example seems most applicable. Although he points to standard deviation as the tell-tale flag here, this is sort of misleading. The exact numerical value for the standard deviation across all queries is meaningless here, as not every query has an equal likelihood of being slow. As he states, the real problem was the terrible performance of an single query.
How many similar queries exist? Will a new query added to the system trigger a similar bug? We don't know, and standard statistics isn't going to help us unless we have an understanding of the underlying mechanism. The key here is not to test a statistically significant subset of all possible queries, but to check the performance of the actual queries executed (as he did).
On a superficial level, if you are doing overnight processing of log files, then you probably care more about throughput than latency. In this case, averages are probably a fine metric. On a slightly deeper level, standard deviation is only a useful measure if the distribution is known, and in a lot of real world cases it is not. The right question isn't whether 100 or 1000 tests on the same data provides sufficient statistical power, but whether range of inputs is sufficient to trigger worst case perfomance.
Now, I presume that Zed knows these things and applies them appropriately, but the article strikes me as more snide than helpful. Perhaps as others say he's a great guy in person, but I prefer my stats with less attitude and more insight. Here, for example: http://yudkowsky.net/rational/bayes
[edit: changed my sloppy language from 'has no meaning unless to the distribution is normal' to 'is only a useful measure if the distribution is known']