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Stack's manifesto offers insight into his personal journey as a tax protester - and into the large and growing movement that attracted him. Passages of Stack's manifesto suggest that he was involved in a notorious home church scheme that was popular in the part of California where he lived before he moved to Texas, MacNab said. Stack wrote that he was part of a group who held tax code readings and "zeroed in on a section relating to the wonderful 'exemptions' that make institutions like the vulgar, corrupt Catholic Church so incredibly wealthy." He said they had "the best high-paid experienced tax lawyers in the business." MacNab said Stack likely was referring to a notorious scheme run by lawyers William Drexler and Jerome Daly. It was based on the idea that citizens could establish themselves as a church and gain the same tax exemptions afforded to religious institutions. The scheme didn't work, and Drexler and Daly were disbarred and imprisoned. If this was the operation Stack was referring to, it may have been a turning point in his life. He wrote: "That little lesson in patriotism cost me $40,000+, 10 years of my life, and set my retirement plans back to 0. It made me realize for the first time that I live in a country with an ideology that is based on a total and complete lie." This inspired him to take action, write to politicians and meet with likeminded anti-tax protesters. He wrote: "I spent countless hours on the L.A. freeways driving to meetings and any and all of the disorganized professional groups who were attempting to mount a campaign against this atrocity." His anti-tax and anti-government beliefs may also have been fueled by Section 1706, an obscure and relatively unknown change in the tax code that focused on his industry and went into effect in 1986. Section 1706 essentially removed technical workers like software engineers from a safe haven classification of "self-employed consultant," making it easier for the IRS to challenge how Information Technology companies classified their employers. An association of IT companies and industry professionals, now called TechServe Alliance, was created to protest the changes in tax law that it says singled out the industry. "It made the whole business riskier for people using independent contractors because it favored the so-called employment business model," Mark Roberts, TechServe CEO, told FoxNews.com. "It created havoc on a number of folks." Roberts was quick to condemned Stack's behavior as "an act of a very, very sick individual." "I don't see a long-term lasting effect, just a troubled wayward person acting in response to a legitimate issue. But I don't think that actually impacts the issue," Roberts said. Noting that Section 1706 was passed years ago, he added: "We still resent the fact that it singles out the industry, but folks have basically learned to adapt. It's kind of been awhile since this was a burning issue in the industry."

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.