['lizbeth]

and, of course, the accompanying commentary from GDP so that we can understand it . . .

“p-values”
Gregory Pawelski says:
October 17, 2012 at 1:15 AM
Using only FDA-approved, standard lung cancer drugs available to all oncologists, this process of laboratory selection provided a 64.5 percent response rate – more than double the national average of 30 percent (p = 0.00015), well established in the literature. More importantly, the median overall survival of 21.3 months was nearly two-fold longer than the best results of 13.5 months reported for non-assay based standard treatments. Strikingly, among the Stage IV (metastatic) patients, there are several who remain alive approaching eight years since diagnosis.

Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. To wit, a p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, they are reporting a paper in lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015. That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance.