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.
John Smith on 17 Sep 14The seminar begins with an examination of the fundamental vocabulary and concepts related to metrology. Topics include: accuracy, precision, calibration, and "uncertainty ratios". Several of the standard methods for analyzing measurement variation are then described and explained, as derived from AIAG's Measurement System Analysis reference book. The methods include: Gage R&R (ANOVA method, for 3 gages, 3 persons, 3 replicates, and 10 parts), Gage Correlation (for 3 gages), Gage Linearity, and Gage Bias. The seminar ends with an explanation of how to combine all relevant uncertainty information into an "Uncertainty Budget" that helps determine the appropriate width of QC specification intervals (i.e., "guard-banded specifications"). Spreadsheets are used to demonstrate how to perform the methods described during the seminar.
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