Its a dangerously mixed blessing, moreover, that statistical tools are constantly getting easier to use. When anyone can find the best straight-line fit to a collection of data points, its inevitable that more relationships will be "found" that actually have no statistical significance--and even more whose statistical significance says nothing about a cause-and-effect connection. The other major problem in statistics is everyday misuse of the expression, "the law of averages." Im constantly surprised to hear people use this phrase to suggest that because things have gone one way for longer than we would expect, theyre soon bound to swing in the other direction to even things out. This is bad enough when people think that a coin that comes up "heads" four times in a row is somehow more likely to come up tails the next time; its worse when they dont even consider the possibility that this coin might have heads on both sides, and that the information theyve gathered so far about its behavior should be used to adjust their model of the process.Getting smart about risks, uncertainties, bursts of demand and burdens of inference from data is a professional challenge that deserves our determined response. Tell me how you measure what you dont know.
Ive previously suggested that biology, rather than physics or mathematics, may be the pure science that has the most to offer to our thinking about our future IT systems. If theres one thing that biology researchers can do, its define a "null hypothesis"--a statement that what they hope to find is not, in fact, the case--and then set themselves to the task of proving that null hypothesis wrong. Id rather see a software company, for example, prove that a system is not insecure, rather than demand that skeptics show them where its vulnerabilities lie.