This was actually sent round a couple of weeks ago where I work by one of our executives (I work at a place that has Statistics in its name). I didn't mention

This was actually sent round a couple of weeks ago where I work by one of our executives (I work at a place that has Statistics in its name). I didn't mention it then, because I didn't want to be a debbie downer and hurt everyone's "rah rah statistics yay!" feeling. Or send something round that contradicts an executive :P But the paper seemed dodgy as hell to me. This wasn't solved with Statistics. Nor was it solved by Bayesian statistics. It was solved when people did lots of searching, failed to find anything for a while despite searching where the plane was, and then they eventually found the plane near its last known location despite having already searched there. If anything, the paper was just an application of the texas sharpshooter fallacy. I believe the authors made several models, and then included the one in the paper that showed the result they wanted. See paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370.2913&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370... Indeed, see the graphical representation of the probability distribution in the paper, you'll notice that the plane was found in a section of the probability distribution that had already been searched earlier: an area that should now have a lower probability than the opposite half of the probability distribution if we're using semi-reasonable bayesian techniques. Indeed, this is what we see in figure seven. In figure 8, we see their new probability distribution under the assumption that the beacons did not work at all, where they try to say their method pinpointed where the plane was. But since this was not known at the time, and figure 7 is the far more reasonable bayesian model given previous searches in the area (because figure 8 assumes a 100% probability of both beacons failing, something practically no bayesian would do), i posit that they either made that model after the fact, or they indeed had several models fitting several different scenarios, and after the plane was found, they chose the one that best fit the data post-facto. Odds are, if the plane was found, it would fit one of their scenarios, and they would then write a paper saying how their model was such a success. Figure 7 is the far more reasonable bayesian distribution, and it actually tells you to now search in the wrong area. If they followed bayesian methods, there is in fact a bottom half of their distribution that should of been searched next (where the plane wasn't), and they in fact found the plane in an area that had already been passively searched: an area that should have downplayed by bayesian probabilities for future searches because of the unlikely-ness of this area being searched and yet still finding nothing. I actually like Bayes, A LOT, but this is not a good example, except perhaps of the precept: if you want to find a lost plane, its probably a good idea to start looking near where it was last located.