Last year when a Malaysian airliner (with 239 people aboard) went missing on its way to Beijing, there were difficulties in locating exactly where it went down; this caused researchers to look at similar cases in the past and see if there was anything to be learned. It turns out that in 2009 an Air France flight went missing on its way from Rio de Janeiro to Paris, what followed was an exhaustive 2 year search for the plane that could only locate the plane (by locating its black box) within an area the size of Switzerland; but after scientific consultants applied Bayes Theorem, they found the black box in five days. The main areas that Sharon Bertsch McGrayne (author of “The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy”) says Bayes theorem is ‘a short simple equation that says you can start out with a hypothesis about something- and it doesn’t matter how good it is’. This is an interesting take on Bayes Theorem that I did not take full notice of when initially learning about it class; the adaptability of Bayesian hypothesis, ‘the hypothesis can change and improve and still be used with the theorem’, is its strong point, as the theory begins with an extremely subjective hypothesis that, through exposure to new information, becomes more accurate via modification to that hypothesis. Even though in the case of last year’s downed Malaysian plane, Bayes Theorem was not formally applied, the Bayesian treatment of hypothesis as a fundamental starting point that is adjusted throughout with the introduction of new information appears to be a helpful model for decision making in general; consciously focusing on conditional probability as a path to more practical answers (if x is true, and I know y is true, then what else must be true?).